Marcelo E. Rodríguez-López

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Melodic segmentation is a fundamental yet unsolved problem in automatic music processing. At present most melody segmentation models rely on a ‘single strategy’ (i.e. they model a single perceptual segmentation cue). However, cognitive studies suggest that multiple cues need to be considered. In this paper we thus propose and evaluate a ‘multi-strategy’(More)
This paper introduces an unsupervised model for melodic segmentation that extends a method initially proposed in computational biology. In the model segments are identified as sections of maximal contrast within a musical piece, using for this the Jensen-Shannon divergence. The model is extended upon its original formulation, and experiments to test its(More)
Automatic melody segmentation is an important yet unsolved problem in Music Information Retrieval. Research in the field of Music Cognition suggests that previous listening experience plays a considerable role in the perception of melodic segment structure. At present automatic melody segmenters that model listening experience commonly do so using(More)
This paper describes the design of Tonic, a novel web interface for music discovery and playlist creation. Tonic maps songs into a two dimensional space using a combination of free tags, metadata, and audio-derived features. Search results are presented in this two dimensional space using a combination of clustering and ranking visualization strategies.(More)
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