10 January 2003 Melody Alignment and Similarity Metric for Content-Based Music Retrieval
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Proceedings Volume 5021, Storage and Retrieval for Media Databases 2003; (2003); doi: 10.1117/12.476253
Event: Electronic Imaging 2003, 2003, Santa Clara, CA, United States
Abstract
Music query-by-humming has attracted much research interest recently. It is a challenging problem since the hummed query inevitably contains much variation and inaccuracy. Furthermore, the similarity computation between the query tune and the reference melody is not easy due to the difficulty in ensuring proper alignment. This is because the query tune can be rendered at an unknown speed and it is usually an arbitrary subsequence of the target reference melody. Many of the previous methods, which adopt note segmentation and string matching, suffer drastically from the errors in the note segmentation, which affects retrieval accuracy and efficiency. Some methods solve the alignment issue by controlling the speed of the articulation of queries, which is inconvenient because it forces users to hum along a metronome. Some other techniques introduce arbitrary rescaling in time but this is computationally very inefficient. In this paper, we introduce a melody alignment technique, which addresses the robustness and efficiency issues. We also present a new melody similarity metric, which is performed directly on melody contours of the query data. This approach cleanly separates the alignment and similarity measurement in the search process. We show how to robustly and efficiently align the query melody with the reference melodies and how to measure the similarity subsequently. We have carried out extensive experiments. Our melody alignment method can reduce the matching candidate to 1.7% with 95% correct alignment rate. The overall retrieval system achieved 80% recall in the top 10 rank list. The results demonstrate the robustness and effectiveness the proposed methods.
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Yongwei Zhu, Mohan S. Kankanhalli, "Melody Alignment and Similarity Metric for Content-Based Music Retrieval", Proc. SPIE 5021, Storage and Retrieval for Media Databases 2003, (10 January 2003); doi: 10.1117/12.476253; https://doi.org/10.1117/12.476253
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KEYWORDS
Databases

Multimedia

Algorithm development

Feature extraction

Information technology

Automatic tracking

Detection and tracking algorithms

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