Music structure based vector space retrieval

摘要

This paper proposes a novel framework for music content indexing and retrieval. The music structure information, i.e., timing, harmony and music region content, is represented by the layers of the music structure pyramid. We begin by extracting this layered structure information. We analyze the rhythm of the music and then segment the signal proportional to the inter-beat intervals. Thus, the timing information is incorporated in the segmentation process, which we call Beat Space Segmentation. To describe Harmony Events, we propose a two-layer hierarchical approach to model the music chords. We also model the progression of instrumental and vocal content as Acoustic Events. After information extraction, we propose a vector space modeling approach which uses these events as the indexing terms. In query-by-example music retrieval, a query is represented by a vector of the statistics of the n-gram events. We then propose two effective retrieval models, a hard-indexing scheme and a soft-indexing scheme. Experiments show that the vector space modeling is effective in representing the layered music information, achieving 82.5% top-5 retrieval accuracy using 15-sec music clips as the queries. The soft-indexing outperforms hard-indexing in general.