Proceedings Article•
A feature relevance study for guitar tone classification
01 Jan 2012-pp 211-216
TL;DR: It turns out, that a selection of 505 features out of the full feature set of 1155 elements does only reduce the recognition rate of a linear SVM from 82% to 78% and with the use of a polynomial instead of alinear kernel the recognition rates with the reduced feature set can even be increased to 84%.
Abstract: A series of experiments on the automatic classification of classical guitar sounds with support vector machines has been carried out to investigate the relevance of the features and to minimise the feature set for successful classification. Features used for classification were the time series of the partial tone amplitudes, and of the MFCCs, and the energy distribution of the nontonal percussive sound that is produced in the attack phase of the tone. Furthermore the influence of sound parameters as timbre, player, fret position and string number on the recognition rate is investigated. Finally, several nonlinear kernels are compared in their classification performance. It turns out, that a selection of 505 features out of the full feature set of 1155 elements does only reduce the recognition rate of a linear SVM from 82% to 78%. With the use of a polynomial instead of a linear kernel the recognition rate with the reduced feature set can even be increased to 84%.
Citations
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09 Jul 2019
TL;DR: A model called technique-embedded note tracking (TENT) that uses the result of playing technique detection to inform note event estimation and can nicely recognize complicated skills in monophonic guitar solos and improve the F-score of note event estimating by 14.7% compared to an existing method.
Abstract: The employment of playing techniques such as string bend and vibrato in electric guitar performance makes it difficult to transcribe the note events using general note tracking methods. These methods analyze the contour of fundamental frequency computed from a given audio signal, but they do not consider the variation in the contour caused by the playing techniques. To address this issue, we present a model called technique-embedded note tracking (TENT) that uses the result of playing technique detection to inform note event estimation. We evaluate the proposed model on a dataset of 42 unaccompanied lead guitar phrases. Our experiments showed that TENT can nicely recognize complicated skills in monophonic guitar solos and improve the F-score of note event estimation by 14.7% compared to an existing method. For reproducibility, we share the Python source code of our implementation of TENT at the following GitHub repo: https://github.com/srviest/SoloLa .
19 citations
15 Oct 2013
TL;DR: This paper presents an accurate and robust playing mode classifier for guitar audio signals that distinguishes between three modes routinely used in jazz improvisation: bass, solo melodic improvisation, and chords.
Abstract: When they improvise, musicians typically alternate between several playing modes on their instruments. Guitarists in particular, alternate between modes such as octave playing, mixed chords and bass, chord comping, solo melodies, walking bass, etc. Robust musical interactive systems call for a precise detection of these playing modes in real-time. In this context, the accuracy of mode classification is critical because it underlies the design of the whole interaction taking place. In this paper, we present an accurate and robust playing mode classifier for guitar audio signals. Our classifier distinguishes between three modes routinely used in jazz improvisation: bass, solo melodic improvisation, and chords. Our method uses a supervised classification technique applied to a large corpus of training data, recorded with different guitars (electric, jazz, nylon-strings, electro-acoustic). We detail our method and experimental results over various data sets. We show in particular that the performance of our classifier is comparable to that of a MIDI-based classifier. We describe the application of the classifier to live interactive musical systems and discuss the limitations and possible extensions of this approach.
7 citations
28 Jul 2015
TL;DR: In this paper, the authors examined the change in pulse wave and SpO 2 when listening to music and found that there are features indicating a high correlation between the two signals, and they used the three classical music songs that are believed to cause feelings of fear, happiness, sadness.
Abstract: This paper presents the result of examining the change in pulse wave and SpO 2 when listening to music. The purpose of this study is a fundamental study, in order to confirm whether can be performed emotion estimated by the pulse wave and SpO 2 . Using the three classical music songs that are believed to cause feelings of fear, happiness, sadness, was to get the pulse wave and SpO 2 from 10 subjects. The results of the analysis, we have found that there are features indicating a high correlation.
1 citations
References
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Book•
01 May 1986TL;DR: In this article, the authors present a graphical representation of data using Principal Component Analysis (PCA) for time series and other non-independent data, as well as a generalization and adaptation of principal component analysis.
Abstract: Introduction * Properties of Population Principal Components * Properties of Sample Principal Components * Interpreting Principal Components: Examples * Graphical Representation of Data Using Principal Components * Choosing a Subset of Principal Components or Variables * Principal Component Analysis and Factor Analysis * Principal Components in Regression Analysis * Principal Components Used with Other Multivariate Techniques * Outlier Detection, Influential Observations and Robust Estimation * Rotation and Interpretation of Principal Components * Principal Component Analysis for Time Series and Other Non-Independent Data * Principal Component Analysis for Special Types of Data * Generalizations and Adaptations of Principal Component Analysis
17,446 citations
15 Oct 2005
TL;DR: Principal component analysis (PCA) as discussed by the authors replaces the p original variables by a smaller number, q, of derived variables, the principal components, which are linear combinations of the original variables.
Abstract: When large multivariate datasets are analyzed, it is often desirable to reduce their dimensionality. Principal component analysis is one technique for doing this. It replaces the p original variables by a smaller number, q, of derived variables, the principal components, which are linear combinations of the original variables. Often, it is possible to retain most of the variability in the original variables with q very much smaller than p. Despite its apparent simplicity, principal component analysis has a number of subtleties, and it has many uses and extensions. A number of choices associated with the technique are briefly discussed, namely, covariance or correlation, how many components, and different normalization constraints, as well as confusion with factor analysis. Various uses and extensions are outlined.
Keywords:
dimension reduction;
factor analysis;
multivariate analysis;
variance maximization
14,773 citations
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
Abstract: Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available. These areas include text processing of internet documents, gene expression array analysis, and combinatorial chemistry. The objective of variable selection is three-fold: improving the prediction performance of the predictors, providing faster and more cost-effective predictors, and providing a better understanding of the underlying process that generated the data. The contributions of this special issue cover a wide range of aspects of such problems: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
14,509 citations
"A feature relevance study for guita..." refers methods in this paper
...A detailed general description of algorithms and procedures of feature space reduction is given by Guyon and Elisseeff [6]....
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...[6] I. Guyon and A. Elisseeff....
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TL;DR: A unifying framework for feature extraction from value series is presented and operators of this framework can be combined to feature extraction methods automatically, using a genetic programming approach.
Abstract: Today, many private households as well as broadcasting or film companies own large collections of digital music plays. These are time series that differ from, e.g., weather reports or stocks market data. The task is normally that of classification, not prediction of the next value or recognizing a shape or motif. New methods for extracting features that allow to classify audio data have been developed. However, the development of appropriate feature extraction methods is a tedious effort, particularly because every new classification task requires tailoring the feature set anew.
This paper presents a unifying framework for feature extraction from value series. Operators of this framework can be combined to feature extraction methods automatically, using a genetic programming approach. The construction of features is guided by the performance of the learning classifier which uses the features. Our approach to automatic feature extraction requires a balance between the completeness of the methods on one side and the tractability of searching for appropriate methods on the other side. In this paper, some theoretical considerations illustrate the trade-off. After the feature extraction, a second process learns a classifier from the transformed data. The practical use of the methods is shown by two types of experiments: classification of genres and classification according to user preferences.
169 citations
"A feature relevance study for guita..." refers methods in this paper
...Genetic algorithms can even be employed for feature generation, as described by Mierswa and Morik [12], and by Pachet and Roy [13]....
[...]
...[12] Ingo Mierswa and Katharina Morik....
[...]
01 Apr 2008
TL;DR: There is significant redundancy between and within feature schemes commonly used in practice in practice, suggesting that further feature analysis research is necessary in order to optimize feature selection and achieve better results for the instrument recognition problem.
Abstract: In tackling data mining and pattern recognition tasks, finding a compact but effective set of features has often been found to be a crucial step in the overall problem-solving process. In this paper, we present an empirical study on feature analysis for recognition of classical instrument, using machine learning techniques to select and evaluate features extracted from a number of different feature schemes. It is revealed that there is significant redundancy between and within feature schemes commonly used in practice. Our results suggest that further feature analysis research is necessary in order to optimize feature selection and achieve better results for the instrument recognition problem.
124 citations