Nonlinear audio recurrence analysis with application to genre classification

In this paper we apply nonlinear signal analysis to a music information retrieval task. More concretely, we apply the concept of recurrence plots and recurrence histograms to extract information from music audio frames. We evaluate the effectiveness of this approach with a typical genre classification framework and compare it against a baseline obtained from standard spectrum-based descriptors. The accuracy reached by the histogram-based descriptors alone does not surpass the one achieved by the spectral-based descriptors. However, we show that the combination of both descriptor sources results in consistent improvements up to 5 absolute percent points. This high lights the potential of nonlinear signal analysis for quantitative music description. In particular, it suggests that the information resulting from this approach is complementary to the information obtained through tile commonly used spectral representation.