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Content-Based Music Structure Analysis Using Vector Quantization

Music structure analysis has been one of the challenging problems in the field of music information retrieval during the last decade. Past years advances in the field have contributed toward the establishment and standardization of a framework covering repetition, homogeneity, and novelty based approaches. With this paper an optimized fusion algorithm for transition points detection in musical pieces is proposed, as an extension to existing state-of-the-art techniques. Vector-Quantization is introduced as an adaptive filtering mechanism for time-lag matrices while a structure-features based self-similarity matrix is proposed for novelty detection. The method is evaluated against 124 pop songs from the INRIA Eurovision dataset and performance results are presented in comparison with existing state-of-the-art implementations for music structure analysis.

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AES - Audio Engineering Society