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Automatic Music Summarization Using Similarity Measure Based on Multi-Level Vector Quantization  

Kim, Sung-Tak (The School of Engineering at Information and Communications University)
Kim, Sang-Ho (The School of Engineering at Information and Communications University)
Kim, Hoi-Rin (The School of Engineering at Information and Communications University)
Publication Information
The Journal of the Acoustical Society of Korea / v.26, no.2E, 2007 , pp. 39-43 More about this Journal
Abstract
Music summarization refers to a technique which automatically extracts the most important and representative segments in music content. In this paper, we propose and evaluate a technique which provides the repeated part in music content as music summary. For extracting a repeated segment in music content, the proposed algorithm uses the weighted sum of similarity measures based on multi-level vector quantization for fixed-length summary or optimal-length summary. For similarity measures, count-based similarity measure and distance-based similarity measure are proposed. The number of the same codeword and the Mahalanobis distance of features which have same codeword at the same position in segments are used for count-based and distance-based similarity measure, respectively. Fixed-length music summary is evaluated by measuring the overlapping ratio between hand-made repeated parts and automatically generated ones. Optimal-length music summary is evaluated by calculating how much automatically generated music summary includes repeated parts of the music content. From experiments we observed that optimal-length summary could capture the repeated parts in music content more effectively in terms of summary length than fixed-length summary.
Keywords
Music summarization; Similarity measure; Multi-level vector quantization; Fixed-length music summary; Optimal-length music summary; Mahalanobis distance;
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