In this paper we study the efficiency of support vector machines (SVM) with alignment kernels in audio classification. The classification task chosen is music instrument recognition. The alignment kernels have the advantage of handling sequential data, without assuming a model for the probability density of the features as in the case of Gaussian Mixture Model-based Hidden Markov Models (HMM). These classifiers are compared to several reference systems, namely Gaussian Mixture Model, HMM classifiers and SVMs with “static” kernels. Using a higher-level representation of the feature sequence, which we call summary sequence, we show that the use of alignment kernels can significantly improve the classification scores in comparison to the reference systems.
['Cyril Joder', 'Slim Essid', 'Gaël Richard']
Alignment kernels for audio classification with application to music instrument recognition