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      Inter Genre Similarity Modelling For Automatic Music Genre Classification

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          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.

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          A comparative study on content-based music genre classification

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            Author and article information

            Journal
            18 July 2009
            Article
            0907.3220
            407ecb6c-f73d-440c-a314-55c63d763dfb

            http://arxiv.org/licenses/nonexclusive-distrib/1.0/

            History
            Custom metadata
            9th International Conference on Digital Audio Effects (DAFx-06), Montreal, Canada, September 18-20, 2006
            Dafx 2006 submission
            cs.SD

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