Inter Genre Similarity Modelling For Automatic Music Genre Classification

TL;DR

This paper introduces inter-genre similarity modelling techniques, including iterative and score-based methods, to enhance automatic music genre classification by reducing confusion among genres.

Contribution

It proposes novel inter-genre similarity modelling approaches, IIGS and SMIGS, to improve classification accuracy in music genre recognition.

Findings

Improved genre classification accuracy with IGS techniques

Iterative IGS and SMIGS further reduce genre confusion

Experimental results show promising performance gains

Abstract

Music genre classification is an essential tool for music information retrieval systems and it has been finding critical applications in various media platforms. Two important problems of the automatic music genre classification are feature extraction and classifier design. This paper investigates inter-genre similarity modelling (IGS) to improve the performance of automatic music genre classification. Inter-genre similarity information is extracted over the mis-classified feature population. Once the inter-genre similarity is modelled, elimination of the inter-genre similarity reduces the inter-genre confusion and improves the identification rates. Inter-genre similarity modelling is further improved with iterative IGS modelling(IIGS) and score modelling for IGS elimination(SMIGS). Experimental results with promising classification improvements are provided.