Music Structure Discovery (MSD) for popular music is a well known task in Music Information Retrieval (MIR). The proposed approach tries to find the basic musical structure of a piece of music, by applying a template matching algorithm on a modified, bar level Self Distance Matrix (SDM). Mel frequency cepstral coefficients (MFCC) are used to represent timbral qualities of the audio material while chroma vectors are selected to incorporate pitch and harmonic content. The new idea of template matching instead of trying to find explicit blocks or off-diagonal lines is independent of any specific characteristics of the underlying SDM and can therefore be used on a wide range of different songs.

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We propose a strategy for integrating descriptor-driven transformation into mosaicing sound synthesis, in which samples are selected by taking into account potential distances in the transformed space. Target descriptors consisting of chroma, mel-spaced filter banks, and energy are modeled with respect to windowed bandlimited resampling and mel-spaced filters, and later corrected with gain. These transformations, however simple, allow some adaptation of textural sound material to musical contexts.

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