  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.
