Abstract
As an important way of music retrieval, Query-By-Humming has gained wide attention because of its effectiveness and convenience. This paper proposes a novel Top-K similarity search technique, which provides fast retrieval for Query-By-Humming. We propose a distance function MDTW for multi-dimensional sequence matching as well as a subsequence matching method \(MDTW_{sub}\). We show that the proposed method is highly applicable to music retrieval. In our paper, music pieces are represented by 2-dimensional time series, where each dimension holds information about the pitch or duration of each note, respectively. In order to improve the efficiency, we utilize inverted lists and q-gram technique to process music database, and utilize q-chunk technique to process hummed piece. Then, we calculate the MDTW distances between hummed q-chunks and music q-grams, and we can get the candidate music and their sensitive data areas. We proposes TopK-Brute and TopK-LB Algorithm to search the Top-K songs. The experimental results demonstrate both the efficiency and effectiveness of our approach.
The work is partially supported by the NSF of China (Nos. 61572122, 61272178), the NSF of China for Outstanding Young Scholars (No. 61322208), and the NSF of China for Key Program (No. 61572122).
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© 2016 Springer International Publishing Switzerland
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Wang, P., Wang, B., Luo, S. (2016). Top-K Similarity Search for Query-By-Humming. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9659. Springer, Cham. https://doi.org/10.1007/978-3-319-39958-4_16
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DOI: https://doi.org/10.1007/978-3-319-39958-4_16
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