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Learning to Traverse Latent Spaces for Musical Score Inpainting

Inpainting as a Creative Tool

Inpainting is the task of automatically filling in missing information in a piece of media (say, an image or a audio clip). Traditionally inpainting algorithms have been used mostly for restorative purposes. However, in many cases, there could be multiple ways to perform an inpainting task. Hence, inpainting algorithms can be used as a creative tool.

Figure 1:

Specifically for music, an inpainting model could be used for several applications in the field of interactive music creation such as:

  • to generate musical ideas in different styles,
  • to connect different musical sections, and
  • to modify and extend solos.

Previous work, e.g., Roberts et al.’s MusicVAE, has shown that Variational Auto-Encoder (VAE)-based music generation models show interesting properties such as interpolation and attribute arithmetic (this article gives an excellent overview of these properties). Effectively, for music, we can train a low-dimensional latent space where each point maps to a measure of music (see Fig. 3 for an illustration).

Figure 2:

Now while linear interpolation in latent spaces has shown interesting results, by definition, it cannot model repetition in music (joining two points in euclidean space with a line will never contain the same point twice). The key motivation behind this research work was to explore if we can learn to traverse complex trajectories in the latent space to perform music inpainting, i.e., given a sequence of points corresponding to the measures in the past and future musical contexts, can we find a path through this latent space which can form a coherent musical sequence (see Fig. 4).

Figure 3:

To formalize the problem, consider a situation where we are given the beginning and end of a song (which we refer to as the past musical context Cp and future musical context Cf, respectively) The task here is to create a generative model which connects the two contexts in a musically coherent manner by generating an inpainting Ci. We further constrain the task by assuming that the contexts and the inpainting have a certain number of measures (see Fig. 2 for an illustration). So effectively, we want to train a generative model which can maximize the likelihood of p(Ci|Cp, Cf).

Figure 4:

In order to achieve the above objective, we train recurrent neural networks to learn to traverse the latent space. This is accomplished using the following steps:

    • We first train a MeasureVAE model to reconstruct single measures of music. This creates our latent space for individual measures.
Figure 5:
    • The encoder of this model is used to obtain the sequence of latent vectors (Zp and Zf) corresponding to the past and future musical contexts.
Figure 6:
    • Next, we train a LatentRNN model which takes these latent vector sequences as input and learns to output another sequence of latent vectors Zi corresponding
      Figure 7:

      to the inpainted measures.

    • Finally we use the decoder of the MeasureVAE to maps these latent vectors back to the music space to obtain our inpainting (Ci).
Figure 8:

More details regarding the model architectures and the training procedures are provided in our paper and our GitHub repository.

We trained and tested our proposed model on monophonic melodies in the Scottish and Irish style. For comparison, we used the AnticipationRNN model proposed by Hadjeres et al. and a variant of it based our stochastic training procedure. Our model was able to beat both the baselines in objective test aimed at testing how well the model is able to reconstruct missing measures in monophonic melodies.

Figure 9:

We also conducted a subjective listening by asking human listeners to rate pairs of melodic sequences. In this our proposed model performed comparably to the baselines.

Figure 10:

While the method works fairly well, there were certain instances where the model produces pitches which are out of key. We think these “bad” pitch predictions were instrumental in reducing the perceptual rating of the inpaintings in the subjective listening test. This needs additional investigation. Interested readers can listen to some of these example inpaintings performed by our model in the audio examples list.

Overall, the method demonstrates the merit of learning complex trajectories in the latent spaces of deep generative models. Future avenues of research would include adapting this framework for polyphonic and multi-instrumental music.

An Attention Mechanism for Musical Instrument Recognition

Musical instrument recognition continues to be a challenging Music Information Retrieval (MIR) problem, especially for songs with multiple instruments. This has been an active area of research for the past decade or so and while the problem is generally considered solved for individual note or solo instrument classification, the occlusion or super-position of instruments and large timbral variation within an instrument class makes the task more difficult in music tracks with multiple instruments.

In addition to these acoustic challenges, data challenges also add to the difficulty of the task. Most methods for instrument classification are data-driven, i.e., they infer or ‘learn’ patterns from labeled training data. This adds a dependence on obtaining a reliable and reasonably large training dataset for instrument classification.

Data Challenges in Instrument Classification

In previous work, we discussed briefly how there was a data problem in the task of musical instrument recognition or classification in the multi-instrument/multi-timbral setting. We utilized strongly labeled multi-track datasets to overcome some of the challenges. This enabled us to train deep CNN-based architectures with short strongly labeled audio snippets to predict fine-grained instrument activity with a time resolution of 1 second.

In retrospect we can claim that, although instrument activity detection remains the ultimate goal, current datasets are both too small in scale and terribly imbalanced in terms of both genre and instruments as shown in this figure.

Distribution of instruments in one dataset

The OpenMIC dataset released in 2018 addresses some of these challenges by curating a larger-scale, somewhat genre-balanced set of music with 20 instrument labels and crowdsourced annotations for both positive (presence) and negative (absence) classes. The catch is that the audio clips here are 10 seconds long and the labels are assigned to the entire clip, i.e., fine-grained instrument activity annotations are missing. Such a dataset is what we call ‘weakly labeled.’

Previous approaches for instrument recognition are not designed to handle weakly labeled long clips of audio. Most of them are trained to detect instrument activity at short time-scales, i.e., the frame level up to 3 seconds. The same task with weakly labeled audio is tricky since instruments that are labeled as present may be present only instantaneously and could be left undetected by models that average over time.

In our proposed method, we utilize an attention mechanism and multiple-instance learning (MIL) framework to address the challenge of weakly labeled instrument recognition. The MIL framework has been explored in sound event detection literature as a means to weakly labeled audio event classification, so we decided to apply the technique to instrument recognition as well.

Method Overview

In MIL, each data point is represented as a bag of multiple instances. In our case, each bag is a 10 second clip from the OpenMIC dataset. We divide the clip into 10 instances, each of 1 second. Each instance is represented by a 128-dimensional feature vector extracted using a pre-trained VGGish model. Thus, a clip input is 10×128 dimensional. As per the MIL framework, each bag is associated with a label. This implies that at least one instance in the bag is associated with the same label, we just don’t know which one. To learn in the MIL setting, algorithms perform a weighted sum of instance-level predictions to obtain the bag-level predictions. These predictions can be compared with the bag-level labels to train the algorithm. In our paper, we utilize learned attention weights for the aggregation of instance-level predictions.

attention model

Looking at the model architecture in the figure above, the model estimates instance-level predictions and instance-level attention weights. We estimate one weight per instance per instrument label. The weights are normalized across instances to sum to one, adding an interpretation of relative contribution each instance prediction has on the final clip-level prediction for a particular instrument.

We compared this model architecture with mean pooling, recurrent neural networks, fully connected networks and binary random forests and found our attention-based model to outperform all the other methods, especially in terms of the recall. We also tried to visualize the attention weights and see if the model was focusing on relevant parts of the audio as it was supposed to.

Visualization of attention weights

As you can see in the above image, while this model is not very adept at localizing the instruments (in the first example, violin is actually in all instances, but model applies high weights only to a couple of instances), it does a good job at seeking out easy to classify instances and focuses weights on those.

In conclusion, we discuss the merits of weakly labeled audio datasets in terms of ease of annotation and scalability. In our opinion, it is important to develop methods capable of handling weakly labeled data due to the ease of annotation and therefore scalability of such datasets. To that end, we introduce the MIL framework and discuss the attention mechanism in brief. Finally, we show a visualization of how the attention weights provide some degree of interpretability to the model. Even though the model is not perfectly localizing the instruments, it learns to focus on relevant instances for final prediction.

The detailed paper can be found here.

Explicitly Conditioned Melody Generation

by Benjamin Genchel

The advent of modern deep learning has rapidly propelled progress and attracted interest in symbolic music generation, a research topic that encompasses methods of generating music in the form of sequences of discrete symbols. While a variety of techniques have been applied to tasks in this domain, the majority of approaches treat music as a temporal sequence of events and rely on Recurrent Neural Networks (RNNs), specifically the gated variants Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), to model this sequence. These approaches generally attempt only to learn to predict the next note event(s) in a series given previous note event(s). While results from these types of models have been promising, they train on hard mode relative to human musicians, who rely on explicit, discretely classed information such as harmony, feel (e.g. swing, straight), phrasing, meter, and beat to organize and contextualize while learning.

Our study aims to investigate how deep models, specifically gated RNNs, learn when provided with explicit musical information, and further, how particular types of explicit information affect the learning process. We describe the results of a case study in which we compared the effects on musical output of conditioning an RNN-based model like those mentioned above with various combinations of a select group of musical features.

Specifically, we separate musical sequences into sequences of discrete pitch and duration values, and model each sequence independently with its own LSTM-RNN. We refer to these as Pitch-RNN and Duration-RNN respectively. Then we train a pair of these models using combinations of the following:

  • Inter Conditioning: the pitch generation network is provided with the corresponding duration sequence as input and vice versa.
  • Chord Conditioning: both the pitch and duration networks are provided with a representation of the current chord for each input timestep.
  • Next Chord Conditioning: both the pitch and duration networks are provided with a representation of the chord following the current chord for each input timestep.
  • Bar Position Conditioning: both the pitch and duration networks are provided with a discrete value representing the position within a surrounding bar of the current input token.

For a more clear picture of what we’re doing, check out this architecture diagram!

We trained our models using two datasets (Bebop Jazz and Folk) of lead sheets (data extracted from musicXML files), as lead sheets provide a simplified description of a song that is already segmented into explicit components.

Evaluating generative music is a challenging and precarious task. In lieu of an absolute metric for quality, we characterize the outputs of each trained model using a set of statistical methods:

  • Validation NLL — The final loss achieved by a model on the validation set.
  • BLEU Score — a metric originally designed for machine translation, but deployed fairly commonly for judging sequence generation in general, BLEU score computes a geometric mean of the counts of matching N-grams (specifically 1, 2, 3, and 4-grams) between a set of ground truth data and a data set generated by a model. A BLEU score can be between 0 and 1, with 0 being nothing in common and 1 being everything in common (exactly matching).
  • MGEval — the MGEval toolbox, created by our own Li-Chia “Richard” Yang and Alexander Lerch, and described in an earlier blog post on this page, calculates KL-divergence and overlap scores between distributions of a number of music specific statistical features calculated for a set of ground truth data and generated data. These scores give an idea of how much generated data differs from the ground truth, and further provides insight into how they differ in musical terms.

Validation NLL

The general trend observed was, perhaps unsurprisingly, that the more conditioning information used, the lower the final loss. Additionally, models trained with more conditioning learned faster than trained with less.

BLEU Score

Pitch models generally achieved a higher BLEU score with added conditioning factors, while Duration networks demonstrated the opposite trend — lower scores with added conditioning factors. The highest scoring pitch models had both chord and next chord conditioning, though models trained on the Bebop dataset seemed to generally perform better with current chord conditioning than next-chord conditioning, while models trained on the Folk dataset performed better with next-chord conditioning. The highest scoring duration models for both datasets had no-conditioning at all, and the next highest scoring models had only one conditioning factor.
While a BLEU score close to 1 might be ideal for machine translation, for artistic tasks like music generation where there is no right answer, a score that high hints at overfitting, and thus a lack of ability to generalize what it has learned and create novel melodies in unfamiliar contexts. While pitch models scored between .1 and .3, duration models scored from .5 to .9; that duration models with less conditioning scored higher than those with more may actually indicate that models with more conditioning generalized better, as their scores were closer to .5

MGEval produces a lot of numbers — In our case, 66 for a single model (6 scores each for 11 MGEval metrics we used). We aggregated these results in a way that gives each individual conditioning factor a probability indicating its likelihood of contributing to higher performance. We split our 11 metrics into two categories, pitch-based, and duration-based, and performed this aggregation for each set:

In general, we observed that scores on MGEval metrics improve with the addition of conditioning features, but with these aggregation scores, we were able to ascertain (at a high level) which types of conditioning were affecting what. For both datasets, inter and chord conditioning had strong effects on pitch based features, with bar position conditioning a close third. However, for duration based features, it’s harder to determine. We can see here that for models trained on the Folk set, it’s a bit of a grab bag, but for models trained on the Bebop set, chord and bar position clearly contribute the most to success. This is perhaps due to the much simpler, and more repetitive rhythmic patterns found in Folk music as compared to Bebop Jazz music.

Solid takeaways from this study are that conditioning with chords is important not just for pitch prediction, but for duration prediction as well, though the usefulness of conditioning with the next chord vs. the current chord was harder to determine. More generally, we were also able to gain insight into the relative usefulness of our chosen conditioning set for pitch and rhythm based features. Additionally, we saw that the usefulness of particular features can also be genre dependent. Further investigation, along with a subjective evaluation via listening test is certainly worthwhile.

Check out our paper, the associated website, or the codebase for more details, audio samples, data and other information!

Music Informatics Group @ICML Machine Learning for Music Discovery Workshop

There will be 2 presentations by our group at the ICML Machine Learning for Music Discovery Workshop (ML4MD) this year:

  • Pati, Ashis; Lerch, Alexander: Latent Space Regularization for Explicit Control of Musical Attributes:
    Deep generative models for music are often restrictive since they do not allow users any meaningful control over the generated music. To address this issue, we propose a novel latent space regularization technique which is capable of structuring the latent space of a deep generative model by encoding musically meaningful attributes along specific dimensions of the latent space. This, in turn, can provide users with explicit control over these attributes during inference and thereby, help design intuitive musical interfaces to enhance creative workflows.
  • Gururani, Siddharth; Lerch, Alexander; Bretan, Mason: A Comparison of Music Input Domains for Self-Supervised Feature Learning:
    In music using neural networks to learn effective feature spaces, or embeddings, that capture useful characteristics has been demonstrated in the symbolic and audio domains. In this work, we compare the symbolic and audio domains, attempting to identify the benefits of each, and whether incorporating both of the representations during learning has utility. We use a self-supervising siamese network to learn a low-dimensional representation of three second music clips and evaluate the learned features on their ability to perform a variety of music tasks. We use a polyphonic piano performance dataset and directly compare the performance on these tasks with embeddings derived from synthesized audio and the corresponding symbolic representations.

From labeled to unlabeled data – on the data challenge in automatic drum transcription

by Chih-Wei Wu

Automatic Drum Transcription (ADT) is an on-going research topic that concerns the extraction of drum events from music signals. After roughly three decades of research on this topic, many methods and several datasets have been proposed to address this problem. However, similar to many other Music Information Retrieval (MIR) research topics, the availability of realistic and carefully curated datasets is one of the bottlenecks for advancing the performance of ADT systems.

In our previous blog post, we briefly discussed this challenge in the context of ADT. With a standard annotated dataset (i.e., ENST drums) and a small collection of unlabeled data, we demonstrated the possibility of harnessing unlabeled music data for improvements in the context of ADT.
In this paper, we explore this idea further in the following directions:

  1. Identify the major types of ADT systems and investigate generic methods for integrating unlabeled data into these systems accordingly
  2. Train the systems using a large scale unlabeled music dataset
  3. Evaluate all systems using multiple labeled datasets currently available

The intention is to validate the idea of using unlabeled data for ADT in a large scale. To achieve this goal, we present two approaches.

To show that the benefit of using unlabeled data can be generalized to most ADT systems, the first thing is to identify the most popular ADT approaches. To this end, we reviewed existing ADT systems (for more information, please refer to this recent publication), and we found that the two most popular approaches can be categorized as Segment-and-classify ( classification-based) and Separate-and-detect (activation-based).

Based on these two types of approaches, two learning paradigms for incorporating unlabeled data are evaluated. These are

    1. Feature Learning and
    2. Student Teacher Learning.

As shown in the figure below, both paradigms may extract information from unlabeled data and transfer them to ADT systems through different mechanisms; the feature learning paradigm learns a feature extractor that computes distinctive features from audio signals, whereas the student teacher learning paradigm focuses on generating “pseudo ground truth” (i.e., soft targets) using teacher models and passing them onto student models. Different variants of both paradigms are evaluated.

Results 1: we need more labeled data

In the first part of the experiment results, the averaged performance across all evaluated systems for each labeled dataset (e.g, ENST drums, MIREX 2005, MDB drums, RBMA) is presented. As shown in the following figure, for each individual drum instrument such as Kick Drum (KD), Snare Drum (SD), and HiHat (HH), the averaged performances differ from dataset to dataset. This result not only shows the relative difficulties of these datasets, but also implies the danger of relying on solely one dataset (which is exactly the case in many prior ADT studies). This result highlights the need for more diverse labeled datasets!

Results 2: unlabeled data is useful

In the second part of the experiment results, different systems for each learning paradigm are compared under the controlled conditions (e.g., training methods, the number of unlabeled examples used, etc.). For feature learning paradigm, as shown in the following table, both evaluated systems outperformed baseline systems on SD for the averaged F-Measure. This improvement was confirmed through a statistical check. This result suggests Segment-and-classify ADT systems can successfully benefit from unlabeled data through feature learning.

Role System HH BD SD
Baseline MFCC 0.61 0.62 0.40
Baseline CONV-RANDOM 0.61 0.54 0.39
Evaluated CONV-AE 0.61 0.62 0.42
Evaluated CONV-DAE 0.61 0.61 0.42

For student-teacher learning paradigm, encouraging results can also be found (this time on HH). In the following table, it is shown that all student models performed better on HH compared to both teacher models. This result indicates the possibility of obtaining students that are better than teachers with the help of unlabeled data. Additionally, this result confirms the finding in our previous paper in a larger scale.

Role System HH BD SD
Teacher PFNMF (SMT) 0.47 0.61 0.45
Teacher PFNMF (200D) 0.47 0.67 0.40
Student FC-200 0.56 0.57 0.44
Student FC-ALL 0.53 0.59 0.42
Student FC-ALL (ALT 0.55 0.58 0.44

According to the results, both learning paradigms can potential improve the ADT performances with the addition of unlabeled data. However, each paradigm seems to benefit different instruments. In other words, it is not easy to conclude which paradigm is “the way to go” when it comes to harnessing unlabeled resources. To simply put it, unlabeled data certainly has potential in improving ADT systems, and further investigation is worthwhile.

If you are interested in learning more details about this work, please refer to our paper. The code is available on github.

On the evaluation of generative models in music

by Li-Chia Richard Yang

Generative modeling among creative systems has research interest in a wide variety of tasks. Just as deep learning has reshaped the whole field of artificial intelligence, it has reinvented generative modeling in recent years, e.g., in music or painting. Regardless, however, of the research interest in generative systems, the assessment and evaluation of such systems has proven challenging.

In recent research on music generation, various data-driven models have shown promising results. As a quick example, here are two generated samples from two distinct systems:

Magenta (Attention RNN)
Magenta (Lookback RNN)

Now, how can we analyze and compare the behavior of these models?
As the ultimate judge of creative output is the human (listener or viewer), subjective evaluation is generally preferable in generative modeling. However, the general drawbacks of subjective evaluation can be summarized as various issues related to the required amount of resources and to the experiment design. Furthermore, objective evaluation has the advantage of providing a systematic, repeatable measurement across a significant amount of generated samples.

The proposed evaluation strategy

The proposed method does not aim at assessing musical pieces in the context of human-level creativity nor does it attempt to model the aesthetic perception of music.

It rather applies the concept of multi-criteria evaluation in order to provide metrics that assess basic technical properties of the generated music and help researchers identify issues and specific characteristics of both model and dataset. In a first step, we define two collections of samples as our datasets (in case of objective evaluation, one dataset contains the generated samples, the other contains samples from the training dataset). Then, we extract a set of features based on musical domain-knowledge for two main targets of the proposed evaluation strategy:

Absolute MeasurEments

Absolute measurements give insights into properties and characteristics of a generated or collected set of data.
During the model design phase of a generative system, it can be of interest to investigate absolute metrics from the output of different system iterations or of datasets as opposed to a relative evaluation. A typical example is the comparison of the generated results from two generative systems: although the model properties cannot be determined precisely for a data-driven approach, the observation of the generated samples can justify or invalidate a system design.
For instance, an absolute measurement can be a statistic analysis of note length transition histogram of each sample of a given dataset. In the following figure, we can easily observe the difference of such this feature among datasets from two different genres.

Relative Measurements

In order to enable the comparison of different sets of data, the relative measure generalizes the result among features with various dimensions.
We first perform pairwise exhaustive cross-validation for each feature and smooth the histogram results into probability density functions (PDFs) for a more generalizable representation. If the cross-validation is computed within one set of data, we will refer to it as intra-set distances. If each sample of one set is compared with all samples of the other set, we call it the inter-set distances.
Finally, we measure the similarity between these distributions for the application of evaluating music generative systems, and compute two metrics between the target dataset’s intra-set PDF and the inter-set PDF: the Kullback-Leibler Divergence (KLD) and overlapped area (OA) of two PDFs.
Take the following visualized figure as an example: assume set1 is the training data, while set2 and set3 correspond to generated results from two systems. The analysis can provide a quick observation It can be easily observed that the system that generates generated set2 produces results more in line has a closer behavior in such feature with the training data (in the context of this feature).

Find out more

Check out our paper for detailed use-case demonstration and the released toolbox for further application.

Instrument Activity Detection in Polyphonic Music

by Siddharth Gururani

Most forms of music are rendered as a mixture of acoustic and electronic instruments. The human ear, for the most part, is able to discern the instruments being played in a song fairly easily. However, the same is not true for computers or machines. The task of recognizing instrumentation in music is still an unsolved and active area of research in Music Information Retrieval (MIR).

The applications of such a technology are manifold:

  • Metadata which includes instrumentation enables instrument-specific music discovery and recommendations.
  • Identifying regions of activity of specific instruments in a song allows easy browsing for users. For example, a user interested in a guitar solo or vocals in a song can easily browse to the relevant part.
  • Instrument activity detection may serve as a helpful pre-processing step for other MIR tasks such as automatic transcription and source separation.

In our work, we propose a neural network-based system to detect activity for 18 different instruments in polyphonic music.

Challenges in Instrument Activity Detection

A big challenge in building algorithms for instrument activity detection is the lack of appropriate datasets. Until very recently, the IRMAS dataset was used as the benchmark dataset for instrument recognition in polyphonic music. However, this dataset is not suitable for an instrument activity detection because of the following reasons:

  • The test set contains 3 to 10 second snippets of audio that are only labeled with instruments present instead of a fine-grained instrument activity annotation.
  • The training clips are labeled with a single ‘predominant’ instrument even if more than one instrument is active in the clip.

We overcome this challenge by leveraging multi-track datasets such as the MedleyDB and Mixing Secrets dataset. These multi-track datasets contain the mixes as well as the stems accompanying them. Therefore, annotations for fine-grained stem activity may be automatically obtained by applying envelope tracking on the instrument stems.

In addition, we identify metrics that allow easier comparison of models for instrument activity detection. Traditional metrics such as precision, recall and f1-score are both threshold dependent and not ideal for multi-label classification scenarios. We use label-ranking average precision (LRAP) and area under the ROC curve (AUC) for comparison between different model architectures. Both these metrics are threshold agnostic and are suitable for multi-label classification.

Method and Models

We propose a rather simple pipeline for our instrument activity detection system. The block diagram below shows the high-level processing steps in our approach. First, we split our all the multi-tracks into artist conditional splits. We obtain 361 training tracks and 100 testing tracks. During training, the various models are fed with log-scaled mel-spectrograms for 1 second clips for the training tracks. We train these models to predict all the instruments present in a 1 second clip. We compare Fully Connected, Convolutional (CNN) and Convolutional-Recurrent (CRNN) Neural Networks in this work.

During testing, a track is split into 1 second clips and fed into the model. Once all 1 second level predictions are obtained from the model, we evaluate the predictions at different time-scales: 1 s, 5 s, 10 s and track-level. We aggregate over time by max-pooling the predictions and annotations for longer time-scale evaluation.

As expected, the CNN and CRNN models outperform the Fully Connected architectures. The CNN or the CRNN perform very similarly and we attribute that to the choice of input time context. For only a 1 second input, there are only a few time-steps for the recurrent network to learn temporal features from, hence the insignificant change in performance over the CNN. An encouraging finding was that the models perform well for rare instruments also.

We also propose a method for visualizing confusions in a multi-label context, shown in the figure above. We visualize the distribution of false negatives for all instruments conditioned on a false positive of a particular instrument. For example, the first row in the matrix represents the distribution of false negatives of all instruments conditioned on the acoustic guitar false positives. We observe several cases of confusions that make sense musically, for example: different guitars, tabla and drums, synth and distorted guitars being confused.

For more details on the various processing steps, detailed results and discussion, please check out the paper here! Additionally, a 3 and a half minute lightning talk given at the ISMIR conference is accessible here.

Assessment of Student Music Performance using Deep Neural Networks

by Ashis Pati

Moving Towards Automatic Music Performance Assessment Systems

Improving one’s proficiency in performing a musical instrument often requires constructive feedback from a trained teacher regarding various aspects of a performance, e.g., its musicality, note accuracy, rhythmic accuracy, which are often hard to define and evaluate. While the positive effects of a good teacher on the learning process is unquestionable, it is not always practical to have a teacher during practice. This begs the questions if we can design an autonomous system which can analyze a music performance and provide the necessary feedback to the student. Such a system will allow students without access to human teachers to learn music, effectively enabling them to get most out of their practice sessions.

What are the limitations of the current systems?

Most of the previous attempts (including our own) at automatic music performance systems have relied on:

  1. Extracting standard audio features (e.g. Spectral Flux, Spectral Centroid, etc.) which may not contain relevant information pertaining to a musical performance
  2. Designing hand-crafted features from music which are based on our (limited?) understanding of music performances and their perception.

Considering these limitations, relying on standard and hand-crafted features for the music performance assessment tasks leads to sub-optimal results. Instead, feature learning techniques which have no “prejudice” and can learn relevant features from the data have shown promise at this task.
Deep Neural Networks (DNNs) form a special class of feature learning tools which are capable of learning complex relationships and functions from data. Over the last decade or so, they have emerged as the architecture-of-choice for a large number of discriminative tasks across multiple domains such as images, speech and music. Thus, in this study, we explore the possibility of using DNNs for assessing student music performances. Specifically, we evaluate their performance with different input representations and network architectures.

Input Representations and Network Architectures

We chose input representations at two different levels of abstraction: a) Pitch Contour which extracts high level melodic information, and b) Mel-Spectrogram which extracts low-level information across several dimensions such as pitch, amplitude and timbre. The flow diagram for the computation of the input representations is shown in the Figure below:

Flow diagram for computation of input representations. F0: Fundamental frequency, MIDI: Musical instrument digital interface

Three different model architectures were used: a) A fully convolutional model with Pitch Contour as input (PC-FCN), b) A convolutional recurrent model with Mel-Spectrogram at input, and (M-CRNN) c) A hybrid model combining information both the input representations (PCM-CRNN). The three model architectures are shown below.

Experiments and Results

For data, we use the student performance recordings obtained from the Florida All-State auditions. Each performance is rated by experts along 4 different criteria: a) Musicality, b) Note Accuracy, c) Rhythmic Accuracy, and d) Tone Quality. Moreover, we consider two categories of students at different proficiency levels: a) Symphonic Band and, b) Middle School and design separate experiments for each category. Three instruments are considered: Alto Saxophone, Bb Clarinet and Flute.
The models are trained to predict the ratings (which are normalized between 0 and 1) given by the experts. As baseline, we use a Support Vector Regression based model (SVR-BD) which relies on standard and hand-crafted features extracted from the audio signal. More details about the baseline model can be found in our previous blog post. The performance of the models at this regression task is summarized as the plot in Figure 6. The coefficient of determination (R2) is used as the evaluation metric (higher is better).

Evaluation results showing R2 metric for all assessment criteria. SVR-BD: Baseline Model, PC-FCN: Fully Convolutional Pitch Contour Model, M-CRNN: Convolutional Recurrent Model with Mel Spectrogram, PCM-CRNN: Hybrid Model Combining Mel-Spectrogram and P. Left: Symphonic Band, Right: Middle School

The results clearly show that the DNN based models outperform the baseline model across all 4 assessment criteria. In fact, the DNN models perform the best for the Musicality criterion which is arguably the most abstract and is hard to define. In the absence of a clear definition, it is indeed difficult to design features to describe musicality. The success of the DNN models at modeling this criterion is, thus, extremely encouraging.

Another interesting observation is that the pitch contour based model (PC-FCN) outperforms every other model for the Symphonic Band students. This could indicate that the high-level melodic information encoded by the pitch contour is important to assess students at a higher proficiency level since one would expect that the differences between individual students would be finer. The same is not true for Middle School students where the best models use the Mel-Spectrogram as the input.

Way Forward

While the success of DNNs at this task is encouraging, it should be noted, however, that the performance of the models is still not robust enough for practical applications. Some of the possible areas for future research include experimenting with other input representations (potentially raw audio), adding musical score information as input to the models and training instrument specific models. It is also important to develop better model analysis techniques which can allow us to understand and interpret the features learned by the model.
For interested readers, the full paper published in the Applied Sciences Journal can be found here.

Automatic Sample Detection in Polyphonic Music

by Siddharth Gururani

The term ‘sampling’ refers to the reuse of audio snippets from pre-existing digital recordings with appropriate modifications in new compositions in a way that it fits the musical context. Influential artists that have been sampled frequently by younger artists include, for example, James Brown, Stevie Wonder, and Michael Jackson. Since sampling is an important approach in at least some music genres, there are websites dedicated to linking samples to songs such as whosampled.com. The annotation, however, is done manually by fans and
music aficionados. A system that can automatically detect sampling can help automate this process and could also be used in large scale musicological studies of artist influence across time and geographical space.

The task of automatic sample detection has not been explored in much detail. Some papers proposed methods involving a modified audio fingerprinting method and Non-negative Matrix Factorization (NMF). The block diagram below gives a broad overview of the method used in this work.

flowchart of the sample detection process

The algorithm we present also utilizes NMF and adds a post-processing step with subsequence Dynamic Time Warping (DTW) to extract features that indicate a sample/song pair. The figure below shows a distance matrix for a song in which the sample is looped 4 times in 20 seconds as indicated by the diagonal lines. We extract features from the detected paths and use them to train a random forest classifier.

distance matrix showing 4 repetitions of the looped sample

A new dataset had to be created for the evaluation of the system as previous publications lack systematic evaluation. This dataset originates from whosampled.com and is now publicly available. Our evaluation results, presented in the paper, indicate that our algorithm is has reasonably high precision while suffering from low recall which may be attributed to absence of clear alignment paths in the distance matrix.

For details on the method, results and discussion, please refer to the published paper available here.


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Résultats Finale Régionale d’Halifax 2019

Halifax, November 21st 2019 – Halifax highlighted the talent of the region’s best mixologists at MADE WITH LOVE™, the largest mixology competition in Canada. This Monday, eighteen audacious bartenders competed to offer not just the best, but the ultimate creation that would amaze the epicurean crowd!

The eighteen regional finalists, who had been selected in the qualification round held in May, competed for the titles of Public’s Choice and Judge’s Choice in a truly festive and dynamic atmosphere.

The bartenders doubled with ingenuity so as to not let down the crowd who showed their love and support for the local bartending scene.

The winners? For this very special edition in Halifax MADE WITH LOVE™ is proud to announce that Reid Fraser, representing Lot Six Bar & Restaurant won the Judge’s Choice & the Public’s Choice awards with his Disaronno-based cocktail!

This well-deserved victory will bring him and Taylor MacPherson who ranked second for the public choice, to compete against the best Canadian mixologists at the MADE WITH LOVE™ finale in May 2020, where both finalists will try to bring home the title of Canada’s best bartender!

Official Competition Results:

Judge’s choice:

  1. Reid Fraser – Lot Six Bar & Restaurant
  2. Dylan Brentwood – Lot Six Bar & Restaurant
  3. Will Assaad – Lot Six Bar & Restaurant

Public’s choice:

  1. Reid Fraser – Lot Six Bar & Restaurant
  2. Taylor MacPherson – Sea Smoke Restaurant
  3. Annie Murray – 1862 & Ilya Kravtsov – Lot Six Bar & Restaurant

Reid Fraser’s winning recipe:

Road to Saronno
Pineapple Juice
Handmade non-alcoholic “dry vermouth”
Acid Adjusted Apricot Juice
Herb and Lavender Syrup
Montellier Sparkling Water

Taylor MacPherson’s winning recipe:

Ch-ch-ch-cherry Bomb
Cherry Juice
Lemon Juice
Ginger Syrup
Cherry infused egg whites

Food Pairing:
Sushi roll, Bluefin Tuna, Cream Cheese, Jalapeño, Basil, Disaronno Unagi Sauce, Wasabi Aioli

Congratulations to all competitors and thank you to our valued partners!

Campari Canada
Espolòn Tequila
Flor de Caña
Jura Whisky
Old Forester
Southern Comfort
Tia Maria
Woodford Reserve

Partager sur facebook
Partager sur twitter
Partager sur linkedin
Partager sur email
Made with Love

Made with Love

Créateurs d'événements d’envergure internationale pour les amateurs de cocktails et les épicuriens culinaires. Passion, excellence, extravagance, communauté.

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skill up header

Manufacture Your Future

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  • Intro to Manufacturing and CNC, a seven-week class at Gateway Community College from March 2 through April 17, 2020 in New Haven. Classes are 9 a.m. to 4 p.m. weekdays and registration deadline is January 31, 2020,

The deadline to submit applications for the first two sessions is January 3 and the third session will close on January 31. Applications are now being accepted on a rolling basis. Interested candidates can visit to apply, followed by completing the required math assessment as soon as possible.

Dates for next classes at Gateway Community College to be announced

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  • Good at Math

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Funded by the Apprenticeship Connecticut Initiative. Equal Opportunity Program. Auxiliary Aids Available Upon Request to Persons with Disabilities. Contact Disability Resource Coordinator Barbara Green at 203-867-4030 x239 or


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Home HockeyBuzz Register Login
I hate you, Toronto, ON • Canada • 27 Years Old • Male
After a few crazy days of free agency, the dust is starting to settle and all that remains are second-tier players. In the midst of this, the LA Kings find themselves with a payroll of $US 28,340,000 million (thanks to Chip's Cap Central) going into next season. While they have six Restricted Free Agents to deal with, Jaret Stoll and Patrick O'Sullivan are the only ones of the bunch that may be getting anywhere in the 2 million vicinity.

After the 2007-08 season concluded, the NHL raised the Cap once again, bringing it up from 52 to 56.7 million, with the cap floor sitting at 40.7 million. Which means the Kings will have to spend a little over 12 million to reach the floor. This might prove to be a difficult task considering that a) none of their remaining players to sign are worth big dollars, and b) there isn't a big variety of Unrestricted Free Agents to fill the void. And you can scratch Ladislav Nagy off that list, after playing for the Kings last season he's decided to test the Free Agent waters.

Here is a list of the remaining RFA's along with their salaries of the 07-08 season:

Jarret Stoll -- 2.2M (3 point decrease with 30 more games played than 06-07)

Patrick O'Sullivan -- 650K (due for a raise after a 52 point, 22 goal season)

Brad Richardson -- 600K

Matt Moulson -- 500K

Peter Harrold -- 500K

Erik Ersberg -- 575K

After looking at this list, it'd be hard to imagine all these players racking up more than around 6 million. Playing along with that figure (and that was generous), that would bring the Kings to about a 34 million payroll, which would leave them around 6 million under the Cap.

With that said, even with these RFA's signed up, the Kings still need to sign a few players. Whether they call-up prospects or sign free agents is up to them, but they better be willing to spend some money, or else they may find themselves having to trade for players with inflated salaries (ahem, Bryan McCabe and Pavel Kubina are available!).

Free Agents the Kings may target:

Pavol Demitra

Jason Williams

Mark Recchi

Brendan Morrison

Owen Nolan

Mike Peca

Brendan Shannahan

Jason Smith
Filed Under:   Kings   Cap   NHL   hockey
July 6, 2008 1:41 AM ET | Delete
Hatcher and his 3.5 million would help.... Hextall needs to call Holmgren
July 6, 2008 3:04 AM ET | Delete
guess no one wants to sign there
July 6, 2008 6:40 AM ET | Delete
How about taking Khabibulin off Chicago's hands.
July 6, 2008 8:49 AM ET | Delete
How the Kings and Hawks haven't made a Khabibulin trade yet is strange.
July 6, 2008 11:25 AM ET | Delete
Excellent, thought provoking blog RR. Bravo.
July 6, 2008 5:20 PM ET | Delete
Using nhlnumbers.com The Kings have 13 players signed with a cap hit just over 25 million. That means 10 players need to make up the other 15 million. Not too hard to do, especially if they place Cloutier and his 3 plus million dollar cap hit on the NHL roster. Add Stool and thats about 5 million right there. I expect the Kings to sign Montador and a top 9 forward. The owner has put a limit of about 4.5 million on a salary paid to any player. That is why Khabibulin will not be traded for and the same reason Vishnovski was traded.
July 6, 2008 7:51 PM ET | Delete
O sully is up for a nice raise....maybe ...3-4 mill...At least the kings are not over paying the market!!! Dean Lombardi is predicting a 41-43 mill payroll. We can go after Bertuzzi...at 4 million...LOL what a waist of money...
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I have written several posts this month (January 2012) that focus on my tendency towards impatience and how that plays out in the many facets of my life. Being close to the wildness and flow that is found in natural settings like forests has always been an inspiration for stillness, calm to enter my life. Water flowing in creeks, rushing roughly in waterfalls or just gently flowing in bodies of water stimulates quietness in me that I love. It is a way for me to achieve a sense of patience.  It is about reaching a place where I am able to wait for things to unfold more naturally and with less interference or effort. It is about being content with stillness. It is about learning to be with the magic that is this life.

We can sometimes get a sense of how content we are by noticing how restlessly we are searching for new things: adventure; material objects; relationships; power; status; money and so on. This can be difficult to observe as it is so commonplace in terms of expected behaviour in today’s society. We are always running after some new thing, new goal, new achievements and on and on.  The push to make plans for the future, to save money for the future and constantly improve ourselves is ever-present. We are supposed to have plans for the future.

If our plans for the future are not drastically different from our normal activities this could mean that we are content with the way things are. That by no longer searching for new and more we have reached a point of contentment. This may appear to be the same as being patient or reaching a point of stillness and thus be seen as success.  Through a western world lens of outcomes and goals an endpoint of stillness may look like an accomplishment to be strived for.

From my perspective stillness by its very nature is not something to be accomplished but is a way of travelling along the journey. The water flowing in the creek or in the waterfall prompts stillness yet it is the flow, the movement of the water that is doing the prompting. If the stillness were to be achieved and checked off as completed the flow would be gone. If we grab onto stillness and hold on tight we lose it.

an impasse?

This is a long way of getting to the idea that if we believe we have reached a point of stillness and contentment in our lives we may in fact have only reached an impasse.  An impasse of the potential for progress through the journey of our lives. We may be mistaken by the lack of searching and believe that this is a good thing, that we have reached a pinnacle of stillness or calm. I challenge this notion and wonder instead if it could indicate that we are allowing our level of comfort to block our progress or any challenge of the status quo. Rather than viewing stillness and patience as an avenue for relaxation and reduced activity perhaps could we see it as a tool to help us move into a broadened perspective and participation in the world.

Can we see stillness as a mechanism to bring us closer to an expanded awareness? Is there value in moving beyond a position of comfort to consider the broader world? Can our level of contentment support us in that venture?

Irene McDermott © 2012


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Suddenly Cilantro

suddenly cilantro

Maybe spending eight years in New Mexico has caught up with me. Or maybe the influence of the 100+ degree weather has overridden my usual sense of taste.

Suddenly, I’m craving cilantro!

Not that there’s anything wrong with cilantro. I wouldn’t say I dislike it. I just don’t choose it for my menus. Ever.

Until now!  I have been creating dish after dish with cilantro and absolutely loving the fresh, bright, green refreshing spark it adds as a garnish to pinto beans, in couscous salad, in pico de gallo and again today in tabbouleh salad.

The result?  Lovely tabbouleh salad with plenty of veggies, chlorophyll from the greens, and a delightful sweet and sour dressing.  And no cooking needed on this toasty hot New Mexican day.

Tabbouleh Salad (serves eight)

1 1/2 cups bulgur wheat and boiling water

Put the bulgur in a bowl and pour enough boiling water over it to cover 1/2 inch above the bulger. Let it sit until all the water is absorbed, about 20 minutes.  Let the bulgur cool and fluff it with a fork.

Add diced veggies to the bulgur. Typically recipes have onions, cucumbers, tomatoes and parsley with a lemon and oil dressing. But don’t feel restricted! Choose whatever you want to use.

On this particular day I needed to use up what was in my refrigerator. I diced one large cucumber, half a red onion, one yellow squash, one bunch of parsely and yes, one bunch of cilantro.

Tip: Wash the parsley and cilantro and spin them in the salad spinner to get them dry. Remove the thicker stems and don’t worry about the small tender stems then gently chop them up.

Dress with a citrus dressing.  Instead of the standard lemon and oil, I used tahini, white wine vinegar, a teaspoon of tart cherry concentrate, juice of an orange, salt and one clove of crushed garlic.

Vegan Chilled Cucumber Fennel Soup

Lately, it’s been too darn hot to cook!

Summer has hit very hard around here with temperatures over 100 degrees for the last several days. On top of that we are having the worst drought in history with no prediction of when relief will come.

What’s a cook to do?

One thing I have been doing is getting up early enough to cook something in the morning when the air is . . . well I wouldn’t go so far as to say “cool” . .  but I figure early a.m. is when the temp is as low as it’s going to get for the day. Even so, I do not want to fill our home with heat from my stove and oven!

The other evening I was through with work and even though it was rather late evening, it was still mucho calor fuera and even though I had not eaten since about 11 a.m. (only breakfast and second breakfast). I still didn’t want to eat anything. Just too hot! But I knew I would eventually get hungry.

(And that’s the thing about summer, don’t eat, don’t eat, too hot to eat, and then Wham! Starving! Eat Everything!. I mean, what’s with ‘second breakfast’ lately? What am I, a Hobbit?  I’ll let you know if I ever get that under control.)

Mucho calor fuera!
Mucho calor fuera!

I thought, “I should eat something at least. What could I possibly make that I might feel like eating?”

There it was! A visionary flash of the ultimate cool-as-a-cucumber but loaded with savory, satisfying flavors.
There it was—a visionary flash of the ultimate cool-as-a-cucumber-but-loaded-with savory-satisfying-flavors summer refreshment idea!

“How about a cold, creamy cucumber soup?”

So I got busy. There was a little cooking involved, but not much.

1. I minced a little red onion—very fine mince—and sauted that with a touch of olive oil until it was sweet and tender.

2. I added some soup stock. I used Imagine Foods Vegetable Soup Stock which is the only ready-made stock I have found that does not have cane sugar in it. (More notes about the soup stock later.)

3. I grated a bulb of fennel and a cucumber—both organic—and added that.  I added salt, white pepper and then a little unsweetened coconut milk and some nutmeg. I adjusted the salt and then chilled the soup. Surely you could play around with the seasonings. A bit of cardamom perhaps? Or go for a hit of hot chili pepper?

Lovely chilled soup in three four easy steps!

4. Use the fluffy end of the fennel as a garnish. (That is, if you can. The fluffy tips of the fennel look lovely when dry and seem like the perfect garnish. But as soon as they get wet they rapidly collapse and resemble a wet Shih Tzu.  So throw on that garnish and serve it fast!)

Now let me talk to you about the color.  If you buy one of these ready-made soup stocks it’s going to be orangey-yellow because they use a lot of carrots or squash. And the flavor of this stock is very nice but definitely affects the outcome of the soup.

If you think creamy cucumber soup should NOT look like pea soup, and you want a more traditional white-looking soup, you should make dashi as your soup stock.  This is not hard to do at all. Then you will have a clear broth to start with. Further, if you really want a whiter soup, you could peel the cucumber before grating it and then you would have much less green color.

I’m sure my photo of this soup would be much more picture perfect had I done that but I was looking for something very fast so used the packaged broth and I prefer to use whole organic foods whenever possible so I generally don’t peel my cucumbers.  There’s lots of good stuff (nutrients) right under that skin!

When the soup was chilled, I served it to my Hubbin’ and myself. I thought probably the soup would be too mild for him and he might not like it. Wrong!! He did like it very much! We both enjoyed the flavors and felt refreshed after eating it.

Another tip about this soup is that you can keep it chilled in a container for work or travel and enjoy it as is! How simple is that?

Quick Cooking Tip – Parsley!

Here’s a little tip for getting the most out of your cooking time, especially in the summer when most of us would really like to create fast, bright, nutrition-packed dishes without using a lot of heat.

The tip is:  P A R S L E Y

Parsley (Photo credit: Wikipedia)

This is the stuff that nearly every restaurant uses for a garnish on your plate.  You know, the little green sprig of greens on a tiny stem that add color to the otherwise medium-dull plate of food.

I had never thought of parsley as a vegetable, as in—something you actually eat—until I met my friend Carolyn years ago and went to her house for lunch.  She served parsley sandwiches!  She used some homemade whole-grain bread with a bit of mayo, sliced tomatoes from her garden, a few slices of red onion and a huge bunch of parsley.

This turned out to be a delightful sandwich!  I have indulged in parsley sandwiches ever since.  I happen to prefer the “Italian” parsley which is the flat-leafed variety.  And yes, if you do eat the little sprig served as a garnish, chlorophyll-rich parsley will help you cleanse your breath.  Parsley is often eaten raw but it can also be added to cooked dishes.

English: Petroselinum crispum, Parsley, leaf. ...

English: Petroselinum crispum, Parsley, leaf. Deutsch: Petroselinum crispum, Apiaceae, Petersilie, Blatt. (Photo credit: Wikipedia)

Parsley can be washed, cut and dropped into a dish you are cooking and will rapidly turn bright green.  When it is bright, it is done.  This delicate culinary herb takes almost no cooking at all yet it has powerful nutritional properties.  Usually I add parsley to a dish I’m cooking and turn off the stove. Gorgeous!

Parsley is a nutritional powerhouse!  For one thing it is a good source of Vitamin K. You can learn more about the vitamins and minerals in parsley here:

If you want to, you can wash an entire bunch of parsley by filling your sink or a bowl full of cold water and immersing the entire bunch still tied together.  Shake it around and get all the sand and dirt out, then drain the bunch and untie it.  That way you will not be chasing around small sprigs of the stuff in your wash bowl.


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Buying A Northern California Ranch – A Wise Or Dumb Idea?

On September 19, 2018 by Himanshu Shah

Northern California Ranch

Real estate investment or buying a property is nothing short of a big decision. You need to look at all the variables and aspects before you even think about becoming the new owner of a Northern California ranch.

But what you want to know whether it is a wise or dumb idea when buying a Northern California ranch. Unfortunately, you won’t find a straight answer, given all the elements involved. So, the best way to decide whether you are making a wise or dumb move is to ask some hard and fundamental questions.

Do You Have The Finances?

You can’t buy a Northern California ranch if you don’t have the money. And there are two common ways ranches are purchased. Firstly, if you have the money in the bank, you can directly pay for the property with cash, and you won’t have to worry about a mortgage.

The second option is to go to the bank and apply for a loan. Naturally, this is not a primary process, and it can take a few days to get finalized.

Either way, you are spending a lot of money, and you need to be sure to have it. For example, if you get a loan from the bank, you’ll be paying for many years to come. Are you in a position to maintain these payments in the foreseeable future? You can use Google to see estimates of monthly payments.

What Will You Use The Ranch For?

There are many reasons why you can buy a ranch. Maybe you want to use it as a vacation home or live there. Or perhaps you want to rent it out to farmers and horse breeders. Yes, there are many ways you can make the ranch pay for itself.

Having a clear idea of why you are buying a Northern California ranch is excellent advice before signing your name on any document. Otherwise, you end up with a piece of property that only costs maintenance. In other words, just buying a ranch for no reason isn’t exactly the brightest idea.

Does The Ranch Have Resale Potential?

In some cases, you want to invest in a ranch and sell it at a later stage. Hopefully, the value of the property will have increased, and you will get more than what you spent on it. But this is a lot easier said than done. This can be better understood by checking out information here:

Predicting the potential value of a ranch is incredibly difficult, which is why you need those plans in place. Get informed and learn more about flipping property if this is something that interests you.

Always Think Twice

As mentioned earlier, buying a ranch in Northern California is no small matter. So don’t let an agent or owner make you feel pressured into providing a decision. There are situations when pressure is used to close a sale, and it’s just unethical. The moment you feel compelled, get out of the deal.

Always think long and hard when it involves something as important as buying a ranch. You won’t just get your money back if you’re not happy with the purchase.

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Handmade Dinosaur Models

MY DINOSAURS is dedicated in handmade dinosaur models for more than 20 years. We have rich experience and have been widely recognized so that our products are exported to the rest of the world. We can make animatronic dinosaurs, dinosaur skeletons, dinosaur costumes, and so on. We specialize in customization. Whatever you want, we are here to meet your needs.


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Something we can start doing anytime!

Reading something like this always brightens my day. 💛

Not New Year’s Resolutions, just new day realizations

  • Less complaints, more actions
  • Situations I can’t control, I’ll let be.
  • If there are work left unfinished past 8PM, then there are. (Discipline on both work-life balance and efficiency)
  • Tweet about negative thoughts and feelings less. It only lets the emotions linger and gives others a peek.
  • Two watches, cellphone, laptop.. Be mindful of the time.
  • Sleep longer, live longer
  • Big or small, give Him thanks

Thank you so much, Lord God. I received an unexpected blessing tonight. It really is true not to give in and give up when challenges arise.. for He has plans. He’s preparing something for us. Thank you. I’ve been breaking for days, wondering how to solve these. You led me to it tonight. It’s a hard call, alike all the other options, but it’s for the best, for now. One down.

“Thank God for what’s happening right now. It may not be good, but thank God.”

I’m just grateful for tints, blushes, and lipsticks that help conceal dull, sick, lonely faces of girls who are either ill or having a bad day.

This friendship’s making me realize how killjoy I am most times and how much better it is to just live, say yes, take risks, be happy.

Have a little faith

No matter how bad your day goes, there’s always someone up there who wouldn’t let it end that way. You just have to take the time to notice the little things.

So far, it has been a struggle to wake up each day and get to work. I have a problem with my status, my workload, my worth, my rights. I’m losing all the motivation to do my responsibilities. I won’t get into details but it’s really a mess right now. And just lately, I’ve convinced myself (with the help of my beloved coworkers) to finally do something about it.. and not just anything, but the biggest risk one could do. I’ve set my plans, but I’m still having difficulties executing them, let alone the fact that aside from my colleagues, none of my friends and family have any idea about what I’m going through. I’m also trying really hard to distract myself from other problems I concurrently have.

There I was, getting out of the building from working late, dreading the thought of going home and not getting a good sleep from worrying and getting up again to face life. I saw the buses filled with people standing and sighed. My mood and the condition of my ride home is just a good combo, isn’t it? Then, as I read the sign from the coming bus I was about to ride, I was surprised it was the one that could take me home easily! Buses like this one never (in my whole experience) come this way! It was such a blessing. I kept sighing in relief as I boarded and comfortably sat.

So I thought He was done surprising me. I read today’s teaching from The Daily Bread and it just strike me right through the chest. He wanted me to surrender all plans I have to him, reminded that He is beside me through it all. It even has the scripture that I have a copy here on my phone as a wallpaper that I use each time I needed it.

“God is within her, she will not fall.” Psalms 46:5

Minutes later, as I was writing down my reflections for today’s reading, the song Heavy played on the bus. This is the one song that really gets to me especially in times that everything feels heavy. I let out a deep sigh. The song was followed by Superhero, one of my all-time motivating songs. I mouthed a “thank You” as I began to realize the consecutive ways He’s been letting me aware of his presence. No matter how hard these days are to me right now, I’m never alone in this fight. So are you.

Another Year

Last September 5, I was so busy, tensed, hyped, but not because it was my day but because it was another day to finish, submit, and defend our thesis. The whole team spent the night at my best friend’s house, which was usual to the PB’s. But in spite of the loads of work to finish, they managed to prepare a surprise for me. Twelve midnight, I walked back from my dormitory with my parents, lights were off, they sang and greeted with a huge box of pizza with lighted candle and an ongoing video from the iPad. I could still remember it like it just happened weeks ago.

Now, exactly a year later, here I am at home, past all the stress from college life, past pre-board, watching series and eating pistachio-flavored ice cream while waiting for the board exam’s results. Who would’ve known it was just half a year ago when I haven’t even have time to care about how I look or how much sleep I got the night before or what would it be like six months later?

I am just so thankful for everything I’ve been through, for another year I was granted, for everything. Also, I’m grateful I realized today that birthdays need not be extravagant, nor spendy, nor have lots of people to remember it every year. I woke up feeling bad about my family arguing, had just seven “greeters,” and spent my day just watching and eating at home, even a few hours alone. I feel happy to be 21 and contented.

My day’s ending in a few hours. All I hope is for another good day next year, better if willed. Who knows? There may be a good reason why today’s #HugAPsychMajorDay. I hope my family’s well and happy, my friends and I have stable jobs, and maybe, just maybe, I’m happy and in love with a man I deserve.. as much as he deserves.

Happy birthday, my dear self!

I wrote this on my birthday. I believe in “everything has a reason” now, more than ever. #HugAPsychMajorDay truly wasn’t a coincidence. The results came the day after. Dad woke me up with the happiest tone of his voice on the phone. And, just two days ago, I attended the Psychometricians and Psychologists’ Joint Oath Taking Ceremonies. I could not explain the joy that overwhelms me. The road is still long. For now, let me leave you with these two words, I’M READY.

Ma. Hyacinth C. Estidola, RPm


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I Want What I Want NOW

So you’re mourning the estrangement or loss of your child or children and you want them back into your life more than anything else that you can think of, right?

Let me ask you something as we go.

Is the child or children that he/she/they is or are now, the same child that you raised and want back into your life?

Think about that question as we go along and don’t fully answer it until near the end, ok?

Do we truly have control over our child/children or do they have the balance of power in their own hands?

As much as we’d love to blame the other parent or someone that your child (adult or not) is with, is it really someone else or did you instil the brains and thinking abilities into your child enough to think for themselves?

That’s a tough thought for most parents to have.  After all, we truly believe that our child or children couldn’t possibly be treating us this way were it not for the input of someone else who is part of their lives now.  It’s a hard concept to grasp ahold of that even if these kids (or adults) fell for other people’s lies or manipulations, they are still not only capable of making their own choices but, they’re also capable of using their own minds to think this through.  More key here is the idea that the love that we had for them must have been shown to them throughout their lives with us therefore, their love for us must also still be there, right?

Again, don’t answer the questions that this raises in your mind yet.  Hang in there and wait until the end or sometime later after you’ve read this and thought about it.

Your Child/Children, Adult or not, is being lied to and, or brainwashed by someone else.

While that may be true and your child has fallen victim to lies and manipulations from someone else right now, the truth is that they are allowing it to happen to them for a reason.  They are not using their own minds to think this through or to realize that you, as their parent(s) is/are the ones who loved and raised them to the best of your ability.  Are they really being brainwashed or are they choosing to stay stuck and away from us because it’s what is easiest for them to do or what they want right now?

Stop and think about this.  People cannot be brainwashed into anything that they don’t truly allow to happen to themselves.  The proof is in their upbringing by you.  Deep down inside of themselves, they have a wealth of memories of the good that you did for them.  It’s this point that should be overpowering whatever mental manipulations that they have had done to them by others.

*The reality though is, they are getting something in some way out of allowing this type of scenario to occur.*

What could they be getting out of this though?

Don’t underestimate your child or children’s senses of what’s being done for them or to them by someone else.  If your child or children (again, grown or not) are not responding to you, there’s something better in being in the situation that they’re in and they’re remaining stuck in that spot or place because they want whatever it brings to them.  If they didn’t like it or weren’t getting something from it, they’d be making different choices.

Take a look at an adult child who has taken on a creep of a partner, spouse or significant other.  Staying away from you and siding with their partner may be part of them being closer to that person.  In other words, you’re less important at this point in time than having peace with their significant other.  Now, lest you be thinking “I want to get rid of this person then,” think again.  The reality is, it’s not truly the other person but, rather your child’s quest to get what feels good to them.  You’re considered “collateral damage” in this case.  Your child’s wants and needs are going to follow whatever roads they have to go down in order to get what they want at the moment.  It’s what feels good to them.  You’re not likely the focus of their actions or lack of them nor, are you that important to be above and beyond what your child wants.  This is the sign of a child who may have had you do and give too much for and to them versus anything else that you can think of having done or not done to deserve this type of treatment from them.

I can’t see my child being that selfish though!

Really?  You can’t see that your child, grown or not, would do this to you by being “selfish” enough to want what they want and leave you to eat their dust?

Think about what’s just been said here by you and even me.

Your child (like everyone else) is out to please themselves first and foremost even if that means that they have to oust others from their lives and make them seem like The Devil Incarnate in order to please someone else that they’ve chosen to be with.  Even more shockingly, they are only too pleased to have someone else help them come up with even an imaginary story so that it justifies their actions or lack of them with you.  Everyone wants what they want or they want what makes them feel good.  If someone else can give them what they want, they’re going down that path and you’re going to get shoved aside like the running of the bulls in Pamplona.  It’s that simple.

Someone else is telling my child that I’m a bad person and need to be stayed away from and my child believes it.

There are all sorts of reasons why someone else may be telling them things about you that makes you seem to your child like you’re The Monster From The Deep Lagoon.  The reality is, if your child weren’t getting something out of this in some way or another, your child (as long as they’re old enough to think for themselves) is treating you this way because they want to believe what they’re being told about you.  It justifies something that they’re getting from the situation.  Were they to not be getting something good from it all, they’d be thinking for themselves and acting quite differently than they are behaving towards you.

In all honesty, the person doing the bad mouthing has only so much power over your child.  If your child doesn’t want that kind of power over them, they’d be making other choices.  Your child is making those choices, not the other person or people.  Pathetically, that’s the reality behind what your child is doing or not done.

Don’t make the mistake of thinking that kids (adult or not) aren’t on the internet, doing what you’re doing and finding a world of others who will commiserate with them and aid the process along further towards battering you to a pulp.

One look-up on Google or some other search engine will bring up a plethora of books, forums and other sources for any child, adult or not, who wants to believe that their parents are horrible people who have abused or neglected them.  It’s like giving a gun to your child and the ammunition for it at the same time.  The words “Narcissist” and “Toxic” have been volleyed around by these kids like it’s a sand volleyball tournament.  They love those 2 words because it not only justifies their estrangement or distance from their parents but, it also gives them the target practice and courage to pull that trigger on you and fire away at you.  It matters not that you were likely a normal parent, doing normal parenting things or giving your child everything you could afford to give to them.  All that matters to them is what is happening in their own lives right now and what they are getting out of it all.  Not much else matters to them.

You’re making kids all out to be bad, selfish people.  My child isn’t that way!

While there are cases where parents are narcissists or toxic to their children, more often than not, children who haven’t been horribly abused or neglected to the point of their safety are more or less, there to serve their own needs.  A lot of abused and neglected children are still with their parents or having contact with them on a regular basis.  How ironic is that?

All kids aren’t selfish or bad but, the ones who go to the extents of estranging themselves from their parents without true forms of abuse, neglect or safety, are staying away from their parents and justifying doing so because it’s what suits them or what’s easiest for them to do and be doing.  It’s not rocket science or brain surgery to know that a child will seek out what they want first and foremost and if they can find that in someone else instead of you, they will go where their bread is buttered and life is good or at the least, easiest for them to go.  Your child is no different.  They are getting something out of their estrangement from and treatment of you.

Let’s get back to the first question now that was asked here.

“Do I really want the child my child has become back in my life as is?”

That answer is going to be individual and dependent upon your personal circumstances, of course but, suffice it to say that if you answered that you’d like your child back “as is” right now, you’re self-punishing in a sense.  The child that you think that you want back in your life isn’t that child anymore or they wouldn’t have estranged from you in the first place.  Think about that one for a moment.  Would the child that you gave birth to or raised truly be doing this to you?  Are you remembering your child as they were or as they are now?  What kind of a child would do that to his/her parent(s)?  The only child or children that I know of who would do this to their parents are those who are getting something else from something or someone else.  To me, that’s a selfishness that isn’t something that anyone should want in their lives.  Unless they were to wake up and realize how poorly they’ve treated you, I would think that most parents would NOT want that type of person as part of their lives.

From my little corner of life to yours, while I struggle daily with this issue, I also realize on a rational basis that my little darling isn’t the one I remember now.  She’s someone I don’t recognize.  The child I raised, loved and took care of, giving her everything that I could give to her and more (while sacrificing my own needs to fill her wants) wouldn’t do something like this.  Can I blame the boyfriend she’s been living with for over 9 years now? Yes but, I’d be wrong in doing so.  He’s a real mental health issue but, in speaking openly and honestly, I also see where my own daughter has her own flaws, faults and warts in all of this.  Most of all, she’s using HIS brains and not her own.  Were she to use her own thinking instead of his to justify what she’s doing to not only us but, herself as well, she’d be making an entirely different set of choices.  Like I’ve said above though, she’s getting something from this situation or she wouldn’t be doing it.  That’s just my thoughts on the matter.  You have a right to your own.

Be well.  Love and Light,

Have a GREAT day or evening if you can.

6 thoughts on “I Want What I Want NOW

Add yours

  1. Well, you’ve summed up my thoughts about this estrangement stuff perfectly. This is the conclusion I have come up with also. I do not want this person back in my life because she has shown me her heart, and what she is capable of doing to others. I will love the little girls I knew but they grew into women I cannot be friends with. They have weaponized innocent children in the process, Thats huge.

    Liked by 1 person

    1. Karen, I’m so sorry that as a parent, we have to go through this type of thing. Not liking who our children have become because they are not like the child that we raised, is not comfortable in our feelings, is it? The sad thing is that these “kids” tend to demonize us as adults and turn us into The Devil Incarnate. Do they really feel that way? Well, they or someone that they are with as well as growing up years and Society have spun them into the people that they are today and they likely do believe their own stories that they’ve fabricated as well as being the people that they are today. HUGS XO

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HeadExam last won the day on October 13 2019

HeadExam had the most liked content!

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About HeadExam
  • Rank
    Advanced Member
  • Birthday October 12
Profile Information
  • Gender
  • Location
    Oologah, Oklahoma Indian Territory
  • Interests
    Collecting 14 and 16 hp single cylinder hydro garden tractors from 1969-1979 and Vintage Homelite Chainsaws
Recent Profile Visitors
4,938 profile views
  1. I wish everyone here good luck and hate to leave the site, but for one reason or another I am not able to login anymore without emailing for another new password. This has happened nearly a dozen times after clearing all browsing data and cookies and it continues to happen. It could be on my side or on the server side, I don't know. I just don't have the time or patience to recover my password every time I want to visit the site. Good luck to all.
  2. Yes I've identified an o ring in the pump that is likely shot or broken. PITA to remove the rear PTO housing and three point to gain access
  3. I bought this 55hp diesel tractor and believe it was a good deal at 3k even with the lift issue. I have a very nice gentleman from Wales that is helping me an we believe it is a O ring in the pump. Bit of a ordeal to remove the PTO housing and getting to the pump, but not as bad as many GT repairs I've done.
  4. I saw this little guy, Interesting https://ohloveit.net/collections/tools/products/portable-gravity-sandblaster?fbclid=IwAR0d8mWt_kQEVyDH5TAgNBBxtYNIfQTOCSUfe8G4YZctuZzErt9jJ0n6MLY
  5. Another issue is claimed CFM from the manufacturer, don't believe any of the new ones. Try to find a 1960's or 1970s petrol station compressor, They are big and even if you have to build a doghouse for it outside its worth it and probably cheaper. You can get by with 10 CFM with a 80-100 gallon tank
  6. Place an inline dryer at 6-8 feet above the cabinet and remove moisture frequently. You really need a very large compressor to run one of these cabinets, 10-20 CFM is often the requirement
  7. HeadExam

    1962 Bolens 600

    Great job on the tractor and video Joseph. I'm inspired, I may make a video tomorrow on my Massey project.
  8. I keep looking for you, best bet is to put a advert on GTTalk and other forums and facebook pages
  9. Happy Birthday men, I sure at your age you are having a very good time
  10. Thanks all, I had a great day relaxing for once. I've been really busy with the farm, machines, and saws, lots of projects going on.
  11. HeadExam

    1962 Bolens 600

    Another great video Joseph. I'm looking forward to watching the progress
  12. Better late than never, I hope you had a wonderful birthday
  13. Have a great day Norm, are you old enough to tilt a pint yet? lol
  14. You look right at home and the Landy suits you well. It will take many many miles to remove the emmi-permanent smile and grin.
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If you watch any TV, you can’t help but have noticed the ads for reverse mortgages. This is one of the retirement schemes that has gained a lot of interest recently. On the surface, if you’re to believe the celebrity endorsements, it sounds like a good deal for retirees 62 and older because it can turn your home equity into income. So, instead of you making mortgage payments to the bank, the bank pays you.

Of course, as they say, the devil’s in the details, and when you look at the details a different picture emerges.

For starters, reverse mortgages are not mortgages, but loans against your home’s equity; loans that eventually have to be repaid. Here are the things the ads don’t tell you.

  1. It’s a loan, like I just said, and the fees related to getting it, such as origination and other fees, are often higher than normal loans, or even home equity loans. The origination fee can be as high as $6,000, and that’s on top of title, appraisal and closing fees.
  2. Because reverse mortgage lenders take more risk on this type of transaction, interest rates are high, averaging 3 to 5 percent plus 1.5% mortgage insurance premium. As long as one of the mortgage holders is living in the house, no repayment has to be made, but the annual interest continues to accrue until the loan is repaid, so depending upon how long you take to repay, you could end up paying back more than the house is worth.
  3. You have to repay the loan in full when you move out of the house or die, or it will be foreclosed and sold. Your heirs won’t be responsible for nonpayment of the loan, but this could make it impossible for you to leave the house to them in your will.
  4. You are considered to have ‘moved out’ of the home if you’re absent for a year. This includes living in a long-term care facility.
  5. If your heirs want the house after you die, they must repay the loan in full; in effect, they must ‘buy’ your house.
  6. Even though you aren’t making mortgage payments, you’re still responsible for property taxes, maintenance, and homeowner’s insurance. If you fail to pay these expenses, the lender can foreclose on you.

So, before you take the plunge and sign up for a reverse mortgage, think long and hard about it. The high fees will come out of whatever amount you’re qualified for, which greatly reduces the amount you actually get. You have a choice of a lump sum, periodic payments, or a line of credit. The lump sum will probably be subject to state and federal taxes as well, so just at a time when your expenses should be low, you’ll be taking on a massive debt burden and you just might get a whopping tax bill on top of all that. If your health declines, and it’s no longer possible for you to stay in your house, but you don’t have the resources to repay the loan, you will lose, and there’s a strong possibility that you won’t be able to leave it to your children or grandchildren.

The ads sound so sincere, but, unfortunately, they’re not telling you the whole story. Reverse mortgages are truly a case of caveat emptor.

Published by Charles Ray


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2 Beautiful fonts – Calibri & Lucida Grande

Don’t you like your computer and laptop screen having those beautiful letters and words which are soothing to eyes? Well I do! No doubt there are many fonts which are really very beautiful and I like to see and use them too. But 2 of the fonts, which I always like as “Default” fonts are […]


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Trigger's Tips - 3.45 Newmarket, Wednesday, October 24 Alright Dave,

Andrea Atzeni missed three rides, including Balmoral Handicap winner Sharja Bridge, at British Champions Day at Ascot on Saturday after being informed, at the last minute, by the British Horseracing Authority (BHA) that he would have to serve both days of a two-day ban imposed by the French racing authority, France Galop, after all. However, the Italian jockey could be in for an altogether more satisfactory, and rewarding, experience at Newmarket on Wednesday, starting with Prefontaine in the British Stallion Studs EBF Nursery (3.45).

Being by Mastercraftsman out of a Hurricane Run mare, Roger Varian’s colt has plenty of stamina in both halves of his pedigree and, unsurprising, has improved with each start as he’s gradually stepped up in distance. He opened his account on his first attempt at a mile and a quarter in a similar race at Nottingham two weeks ago and, although raised 7lb for that success, may be able to continue his progress.

The Rowley Mile Course at Newmarket, on which the ability to see out the trip thoroughly is essential, should play to his strengths and, while the Nottingham form has yet to be fully tested, his previous effort, when runner-up to the 90-rated Cap Francais over a mile at Haydock, looks strong in the context of this race. Fellow distance winners Just Hubert and Fearless Warrior are likely to make sure he doesn’t have things all his own way, but both make their handicap debuts off seemingly high enough marks, so Prefontaine looks a decent bet to follow up his Nottingham victory.

Selection: Newmarket 3.45 Prefontaine to win 10/3

Sea-Bird Sea-Bird only ran in eight career races. However, the French stallion won seven of those contests while finishing second in the other. Although Sea-Bird left a mark on the horse racing sport, not much was thought of the horse early on in his life. In fact, he was just another colt at Haras de Victot in Calvados. However, there was one aspect that did strike those who saw Sea-Bird, especially later on, and it was his long, thick legs.

Trained by Etienne Pollet, Sea-Bird featured in three races in his debut season. The two-year-old stormed to victory in the first two contests, the Prix de Blaison at Chantilly and the Criterium de Maisons-Laffitte. In his final race of 1964, Sea-Bird tasted defeat for the first and only time of his career. He came in second at the Grand Criterium behind his stablemate Grey Dawn. Pollet and jockey Pat Glennon weren’t worried about Sea-Bird’s loss, however. In fact, the two weren’t sure if the stallion was good enough to enter the race beforehand.

Despite the loss at Grand Criterium, Sea-Bird was highly thought of by horse racing aficionados going into 1965. Now three-years old, Sea-Bird won all five of his starts. He won his first two races of the season on French soil before heading to England. There, he won the Epsom Derby in fine fashion after being described as “plain” before the race.

A return to France saw Sea-Bird win the Prix de l’Arc de Triomphe. His win back on French soil changed the views of many nonbelievers, who had written Sea-Bird off as a flash in the pan. The colt that hadn’t been special on Pollet’s farm, was now bigger, stronger and more cocksure than the rest of the racing field; and the stallion continued to prove his racing strengths. Sea-Bird’s presence alone may have won him the Prix de l’Arc de Triomphe.

After the 1965 season, Sea-Bird was retired. Even before his final race at the Prix de l’Arc de Triomphe, Sea-Bird’s future was set. American John W. Galbreath leased the stallion for $1.35m and Sea-Bird took up residence in Kentucky at the Darby Dan Farm.


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Did “The Shadow Brokers” hack NSA cyberweapons worth $500M?

A self-styled hacking group going by The Shadow Brokers have started a tongue-in-cheek media campaign claiming that they’ve penetrated the NSA (or someone like that), and made off with “cyberweapons” that they imply are worth more than $500 million.

Their briefing document is entitled “Equation Group Cyber Weapons Auction – Invitation,” and it claims that they’ve found “cyber weapons made by creators of stuxnet, duqu, flame,” and are ready to sell them on the open market.

They’ve dumped a few files as a taster, but the files not yet released are billed as “better than stuxnet.”

The whole thing is written in a curious style, as though native speakers of English had gone out their way to create a document that reads in a carefully and consistently stilted way, fusing a sort of fake and vaguely insulting pidgin with the faintly annoying diction of Yoda out of Star Wars:

We want make sure Wealthy Elite recognizes the danger cyber weapons, this message, our auction, poses to their wealth and control. Let us spell out for Elites. Your wealth and control depends on electronic data. You see what “Equation Group” can do. You see what cryptolockers and stuxnet can do. You see free files we give for free. You see attacks on banks and SWIFT in news. Maybe there is Equation Group version of cryptolocker+stuxnet for banks and financial systems? If Equation Group lose control of cyber weapons, who else lose or find cyber weapons? If electronic data go bye bye where leave Wealthy Elites? Maybe with dumb cattle? “Do you feel in charge?” Wealthy Elites, you send bitcoins, you bid in auction, maybe big advantage for you?

And this is an auction like no other:

  • The winning bid buys the stash of cyberweapons. The Shadow Brokers keep the money.
  • All losing bids are forfeited. The Shadow Brokers keep the money.
  • If the total bids reach BTC1,000,000, everyone gets all the cyberweapons for free. The Shadow Brokers keep the money.
  • The auction ends when Shadow Brokers feel like it. The Shadow Brokers keep the money.
  • The items for sale in the auction are secret, so you have no idea what you are “bidding” for.

With disarming accuracy, the auction document’s FAQ says:

Q: Why I trust you? A: No trust, risk. You like reward, you take risk, maybe win, maybe not, no guarantees. There could be hack, steal, jail, dead, or war tomorrow. You worry more, protect self from other bidders, trolls, and haters.

When we wrote this [2016-08-16T15:00Z], the Bitcoin address that the crooks have given for sending bids was showing a few confirmed transactions, including one for BTC 1.5 (about $850), with a total transaction value of around BTC 1.6.

(You don’t have to bid a bigger amount than any previous transaction to “win”, just to have paid on a larger amount in total than anyone else.)

Watching that address for a short while revealed few bursts of transactions claiming to have been paid in from bitcoins seized in the Silk Road bust, each for 1/1000th of a Bitcoin.

As you can imagine, those transactions were never confirmed and soon fell off the list.

Truth, as they say, really is stranger than fiction, but in this case we just don’t know how far that “truth” goes.

What’s your take? Hack or fake?

Let us know in the comments… (You may remain anonymous.)


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Persistent Pain

Manual therapy modalities, such as Osteopathy, can be helpful in addressing acute or chronic pain, discomfort, feelings of tension, and restricted range of motion. Combining hands-on treatment (manual therapy) with gradually progressive movement strategies and knowledge about pain can help you to make sense of your pain, and provide you with the tools and guidance to change your experience.

Common pain conditions that osteopathy may be helpful for include, but are not limited to:

  • Fibromyalgia
  • Central sensitization
  • Arthritis
  • Osteoarthritis
  • Headaches
  • Chronic neck and shoulder pain
  • TMJ pain
  • Joint pain
  • Low back pain
  • Chronic pains after illness or surgery
  • Pelvic pain
  • Digestive discomforts

Traveling in London-2


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Buckhead Interior Designers

Full-service, Family Friendly Luxury Interiors

Buckhead Luxury Interior DesignEach home that the Nandina team touches is given a personalized look that meets the needs of our clients through functionality, purpose, quality, and beauty.

Our motto is “Real Life, Real Style,” incorporating the philosophy that kids, dogs, parties, and life do happen; and consequently we believe that no one needs to skimp on glamorous living because of them!

Nandina Home & Design proudly offers both residential and commercial design throughout Buckhead. Our team of interior designers is highly experienced and has worked on luxury projects ranging from color palettes and fabric selection to complete remodels and new construction. An added benefit of working with us is our in-house workroom, which offers full creative collaboration between our design team and our clients. If we can’t find that perfect piece, we’ll custom design it!

If you’re in need of a home that is stylish, timeless, and practical, Nandina Home & Design will work with you, your vision, and the rest of your family to achieve an interior that you will love for years to come.

Nandina Home & Design serves the towns of Atlanta, Sandy Springs, Alpharetta, Roswell, Buford, and Dunwoody.