A Music Recommendation System based on Semantic Audio Segments Similarity

A. Bozzon, G. Prandi, G. Valenzise, and M. Tagliasacchi (Italy)


Contentbased Multimedia Retrieval, Music Recommenda tion, Genre Classification


In this paper we propose a novel approach for content based music recommendation. The main innovation of the proposed technique consists of a similarity function that, instead of considering entire songs or their thumbnail rep resentations, analyzes audio similarities between semantic segments from different audio tracks. The rationale of our idea is that a song similarity and recommendation tech nique, to be more meaningful to the user from a seman tic point of view, may evaluate and exploit similarities on semantic units between audio tracks. Our similarity algo rithm consists of two main stages: the first step performs segmentation of the song in semantic parts. The latter as signs a similarity and recommendation score to a pair of songs, by computing the distance between the represen tations of their segments. To assign the global similar ity and recommendation score, we consider a consistent subset of all the inter-segment distances. By adopting a graph-bases framework, we propose a graph-reduction al gorithm on weighted edges that connect segments of dif ferent songs to optimize the similarity score with respect to our recommendation goal. Experiments conducted on a database of 200 audio tracks of various authors and genres show promising results.

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