Global-Local Similarity Function for Automatic Playlist Generation

Abstract

This paper proposes the Global-Local Similarity Function (GLSF) to exploit the multi-scale cues in track sequences for automatic playlist generation (APG). Unlike previous neighborhood-based methods only looking on local similarities for a given playlist, GLTS is constructed by first modeling the fine-grained audio features of each track, then capturing the long-term relations among consecutive tracks. Specifically, the fine-grained audio features are captured beat-by-beat to represent the rhythmic variation of music. The long- term relations are modeled by a designed track distance constraint (TD-constraint) to alleviate the incoherences and un- smooth transition in track sequences. The fine-grained audio features and TD-constraint are aggregated as the final GLSF by a simple distance function. Objective and subjective evaluations show that GLSF-based APG achieves better smooth transition and ensure the long-term content consistency among the tracks. Furthermore, GLSF yields a better understanding of the sequential relationship between tracks and propose a promising way to improve APG algorithms. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>