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Music and timbre segmentation by recursive constrained K-means clustering

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  • Sebastian Krey
  • Uwe Ligges
  • Friedrich Leisch

Abstract

Clustering of features generated of musical sound recordings proved to be beneficial for further classification tasks such as instrument recognition (Ligges and Krey in Comput Stat 26(2):279–291, 2011 ). We propose to use order constrained solutions in K-means clustering to stabilize the results and improve the interpretability of the clustering. With this method a further improvement of the misclassification error in the aforementioned instrument recognition task is possible. Using order constrained K-means the musical structure of a whole piece of popular music can be extracted automatically. Visualizing the distances of the feature vectors through a self distance matrix allows for an easy visual verification of the result. For the estimation of the right number of clusters, we propose to calculate the adjusted Rand indices of bootstrap samples of the data and base the decision on the minimum of a robust version of the coefficient of variation. In addition to the average stability (measured through the adjusted Rand index) this approach takes the variation between the different bootstrap samples into account. Copyright Springer-Verlag 2014

Suggested Citation

  • Sebastian Krey & Uwe Ligges & Friedrich Leisch, 2014. "Music and timbre segmentation by recursive constrained K-means clustering," Computational Statistics, Springer, vol. 29(1), pages 37-50, February.
    • Handle: RePEc:spr:compst:v:29:y:2014:i:1:p:37-50
    DOI: 10.1007/s00180-012-0358-5
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    References listed on IDEAS

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    1. Leisch, Friedrich, 2006. "A toolbox for K-centroids cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 526-544, November.
    2. Sara Dolnicar & Friedrich Leisch, 2010. "Evaluation of structure and reproducibility of cluster solutions using the bootstrap," Marketing Letters, Springer, vol. 21(1), pages 83-101, March.
    3. Douglas Steinley & Lawrence Hubert, 2008. "Order-Constrained Solutions in K-Means Clustering: Even Better Than Being Globally Optimal," Psychometrika, Springer;The Psychometric Society, vol. 73(4), pages 647-664, December.
    4. Uwe Ligges & Sebastian Krey, 2011. "Feature clustering for instrument classification," Computational Statistics, Springer, vol. 26(2), pages 279-291, June.
    5. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
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    Cited by:

    1. Harald Binder & Hans Kestler & Matthias Schmid, 2014. "Proceedings of Reisensburg 2011," Computational Statistics, Springer, vol. 29(1), pages 1-2, February.

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