Music Genre Classification Using Gaussian Process Models
Konstantin Markov, Tomoko Matsui

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.