Music genre classification based on ensemble of signals produced by source separation methods

Issue title: Knowledge-based Modes of Human-Computer Interaction

Guest editors: Maria Virvou and George A. Tsihrintzis

Article type: Research Article

Authors: Lampropoulos, Aristomenis S. | Lampropoulou, Paraskevi S. | Tsihrintzis, George A.^{; *}

Affiliations: Department of Informatics, University of Piraeus, Piraeus 185 34, Greece | Department of Informatics, The University of Piraeus, 80, Karaoli & Dimitriou St., 185 34 Piraeus Greece

Correspondence: [*] Corresponding author. E-mail: [email protected]

Abstract: In this paper, we present a system for musical genre classification that uses a preprocessing module to separate corresponding audio signals into three source signals. A feature extraction procedure is applied to each separated signal and the extracted features are fed into an ensemble combination of Support Vector Machine-based classifiers for genre classification. For the source separation task, we examine and compare two relevant algorithms, namely Convolutive Sparse Coding and a Wavelet Packets-based algorithm. We evaluate our system on a music database of four hundred music samples from four different music genres. Experimental results show that there is a higher classification accuracy in applying a source separation algorithm before feature extraction.

Keywords: Music genre classification, ensemble, support vector machine, source separation

DOI: 10.3233/IDT-2010-0083

Journal: Intelligent Decision Technologies, vol. 4, no. 3, pp. 229-237, 2010