Musical genre classification is an essential tool for music information retrieval systems and it has potential to become a highly demanded application in various media platforms. Two important problems of the automatic musical genre classification are feature extraction and classifier design. In this letter, we propose two novel classifiers using inter-genre similarity (IGS) modeling and investigate the use of dynamic timbral texture features in order to improve automatic musical genre classification performance. Inter-genre similarity is modeled over hard-to-classify samples of the musical genre feature space. In the classification, samples within inter-genre similarity class are eliminated to reduce inter-genre confusion and to improve genre classification performance. Experimental results show that the proposed classifiers provide better classification rates than the existing methods.
- Inter-genre similarity (IGS) modeling
- Mel-frequency cepstral coefficients (MFCC)
- Musical genre classification
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering
- Applied Mathematics