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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bischl B, Wornowizki M, Borg K (2009) The mlr package: machine learning in R. http://www.algorithm-forge.com/bischl/mlr/
Davis SB, Mermelstein P (1980) Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Trans Acous Speech Signal Process ASSP 28(4): 357–366
Ellis DPW (2005) PLP and RASTA (and MFCC, and inversion) in Matlab. http://www.ee.columbia.edu/~dpwe/resources/matlab/rastamat/, online web resource
Halkidi M, Batistakis Y, Vazirgiannis M (2001) On clustering validation techniques. J Intell Inf Syst 17(2–3): 107–145
Hastie TJ, Tibshirani RJ, Friedman J (2001) The elements of statistical learning. Data mining inference and prediction. Springer, New York
Hermansky H (1990) Perceptual linear predictive (PLP) analysis of speech. J Acoust Soc Am 87(4): 1738–1752
Hsu CW, Chang CC, Lin CJ (2009) A practical guide to support vector classification. National Taiwan University, Taipei, http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
Karatzoglou A, Smola A, Hornik K, Zeileis A (2004) kernlab—an S4 package for kernel methods in R. J Stat Softw 11(9):1–20, http://www.jstatsoft.org/v11/i09/
Klapuri A, Davy M (2006) Signal processing methods for music transcription. Springer, New York
Krey S (2008) SVM basierte Klangklassifikation. Dimplomarbeit, TU Dortmund, Dortmund
Li S (2010) FNN: Fast nearest neighbor search algorithms and applications. http://CRAN.R-project.org/package=FNN
Liaw A, Wiener M (2002) Classification and regression by randomforest. R News 2(3):18–22, http://CRAN.R-project.org/doc/Rnews/
Opolko F, Wapnick J (1987) McGill University master samples (CDs)
R Development Core Team (2009) R: A language and environment for statistical computing. Vienna, Austria, http://www.r-project.org, ISBN 3-900051-07-0
Roever C (2003) Musikinstrumentenerkennung mit Hilfe der Hough-Transformation. Universität Dortmund, Fakultät Statistik, http://www.aei.mpg.de/~chroev/publications/RoeverDiplom.pdf
Slaney M (1998) Auditory toolbox: A MATLAB Toolbox for auditory modeling work version 2. Tech. Rep. 1998-010, http://rvl4.ecn.purdue.edu/~malcolm/interval/1998-010/
Traunmüller H (1990) Analytical expressions for the tonotopic sensory scale. J Acoust Soc Am 88: 97–100
Venables WN, Ripley BD (2002) Modern applied statistics with S, 4th edn. Springer, New York, http://www.stats.ox.ac.uk/pub/MASS4
Walker JS (1996) Fast fourier transforms, 2nd edn. CRC Press, Boca Raton
Weihs C, Reuter C, Ligges U (2005) Register classification by timbre. In: Weihs C, Gaul W (eds) Classification: the ubiquitous challenge. Springer, Berlin, pp 624–631
Weihs C, Szepannek G, Ligges U, Luebke K, Raabe N (2006) Local models in register classification by timbre. In: Batagelj V, Bock HH, Ferligoj A, Žiberna A (eds) Data science and classification. Springer, Berlin, pp 315–322
Weihs C, Ligges U, Mörchen F, Müllensiefen D (2007) Classification in music research. Adv Data Anal Classif 1(3): 255–291
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ligges, U., Krey, S. Feature clustering for instrument classification. Comput Stat 26, 279–291 (2011). https://doi.org/10.1007/s00180-011-0234-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00180-011-0234-8