A toolbox for K-centroids cluster analysis
Computational Statistics & Data Analysis
Feature clustering for instrument classification
Computational Statistics - Special Issue: Proceedings of Reisensburg 2009
Least squares quantization in PCM
IEEE Transactions on Information Theory
Proceedings of Reisensburg 2011
Computational Statistics
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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.