Musical signal segmentation method based on self-similarity matrices

This paper deals with the problem of automated segmentation and structure analysis of musical pieces on the basis of extracted features from audio recordings of the piece. The complexity of musical piece segmentation problems is a consequence of the multiple elements by which the segments differ, and those can be melody, harmony, rhythm, and timbre. Self-similarity matrices (SSM), based on selected features are used as a cornerstone for segmentation. The features used are MFCC (Mel-Frequency Cepstral Coefficients) and chroma features which follow changes in timbre and harmonic structure. A self-similarity matrix segmentation procedure based on straightforward methods for morphological image processing is proposed. The Dynamic Time Warping algorithm (DTW) was used as a tool for evaluating the degree of similarity of the segments. The proposed procedure was applied to 2 pieces of classical music.

M. Ratković, M. Marijan, T. Miljković, M. Bjelić, "Musical signal segmentation method based on self-similarity matrices" 67th Conference on Electrical, Electronic, and Computing Engineering ETRAN.
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