Music genre classification using Gaussian Process models

In this paper we introduce Gaussian Process (GP) models for music genre classification. Gaussian Processes are widely used for various regression and classification tasks, but there are relatively few studies where GPs are applied in the audio signal processing systems. The GP models are non-parametric discriminative classifiers similar to the well known SVMs in terms of usage. In contrast to SVMs, however, GP models produce truly probabilistic output and allow for kernel function parameters to be learned from the training data. In this work we compare the performance of GP models and SVMs as music genre classifiers using the ISMIR 2004 database. Audio preprocessing is the same for both cases and is based on Constant-Q spectrograms. The experimental results using linear as well as exponential kernel functions and different amounts of training data show that GP models always outperform SVMs with up to 5.6% absolute difference in the classification accuracy.