NDLI: Feature clustering for instrument classification

Content Provider	Springer Nature Link
Author	Ligges, Uwe Krey, Sebastian
Copyright Year	2011
Abstract	We propose a method that allows for instrument classification from a piece of sound. Features are derived from a pre-filtered time series divided into small windows. Afterwards, features from the (transformed) spectrum, Perceptive Linear Prediction (PLP), and Mel Frequency Cepstral Coefficients (MFCCs) as known from speech processing are selected. As a clustering method, k-means is applied yielding a reduced number of features for the classification task. A SVM classifier using a polynomial kernel yields good results. The accuracy is very convincing given a misclassification error of roughly 19% for 59 different classes of instruments. As expected, misclassification error is smaller for a problem with less classes. The rastamat library (Ellis in PLP and RASTA (and MFCC, and inversion) in Matlab. http://www.ee.columbia.edu/~dpwe/resources/matlab/rastamat/, online web resource, 2005) functionality has been ported from Matlab to R. This means feature extraction as known from speech processing is now easily available from the statistical programming language R. This software has been used on a cluster of machines for the computer intensive evaluation of the proposed method.
Starting Page	279
Ending Page	291
Page Count	13
File Format	PDF
ISSN	09434062
Journal	Computational Statistics
Volume Number	26
Issue Number	2
e-ISSN	16139658
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-02-03
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Feature clustering SVM Classification Timbre Instrument Music Economic Theory Probability Theory and Stochastic Processes Probability and Statistics in Computer Science Statistics
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty Computational Mathematics

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