Music genre classification applied to bass enhancement for mobile technology

Abstract

The aim of this paper is to present a novel approach to the Virtual Bass Synthesis (VBS) algorithms applied to portable computers. The proposed algorithm is related to intelligent, rule-based setting of synthesis parameters according to music genre of an audio excerpt. The classification of music genres is automatically executed employing MPEG 7 parameters and the Principal Component Analysis method applied to reduce information redundancy. To perform genre recognition k-Nearest Neighbors classifier is used. The VBS algorithm is based on nonlinear device (NLD) or phase vocoder (PV) depending on the content of an audio file excerpt. A soft computing (fuzzy logic) algorithm is employed to set optimum synthesis parameters depending on a given song. To confirm the relationship between genres and preferences of listeners in the low frequency range the pairwise subjective comparison test is carried out. In tests 30 pairs of audio files are employed divided into six popular musical genres. Music excerpts processed by a commercially available bass boost algorithm are used for comparison. Based on the responses of the listeners the statistical analysis is carried out. A short summary is also provided that contains plans for future algorithm development.