The VLDB Journal

, Volume 24, Issue 4, pp 519–536 | Cite as

Embedding-based subsequence matching with gaps–range–tolerances: a Query-By-Humming application

  • Alexios Kotsifakos
  • Isak Karlsson
  • Panagiotis Papapetrou
  • Vassilis Athitsos
  • Dimitrios Gunopulos
Regular Paper

Abstract

We present a subsequence matching framework that allows for gaps in both query and target sequences, employs variable matching tolerance efficiently tuned for each query and target sequence, and constrains the maximum matching range. Using this framework, a dynamic programming method is proposed, called SMBGT, that, given a short query sequence Q and a large database, identifies in quadratic time the subsequence of the database that best matches Q. SMBGT is highly applicable to music retrieval. However, in Query-By-Humming applications, runtime is critical. Hence, we propose a novel embedding-based approach, called ISMBGT, for speeding up search under SMBGT. Using a set of reference sequences, ISMBGT maps both Q and each position of each database sequence into vectors. The database vectors closest to the query vector are identified, and SMBGT is then applied between Q and the subsequences that correspond to those database vectors. The key novelties of ISMBGT are that it does not require training, it is query sensitive, and it exploits the flexibility of SMBGT. We present an extensive experimental evaluation using synthetic and hummed queries on a large music database. Our findings show that ISMBGT can achieve speedups of up to an order of magnitude against brute-force search and over an order of magnitude against cDTW, while maintaining a retrieval accuracy very close to that of brute-force search.

Keywords

Subsequence matching Query-By-Humming Indexing Embeddings 

Notes

Acknowledgments

The work of I. Karlsson and P. Papapetrou was supported in part by the project “High-Performance Data Mining for Drug Effect Detection” funded by Swedish Foundation for Strategic Research under grant IIS11-0053. The work of V. Athitsos was partially supported by National Science Foundation grants IIS-0812601, IIS-1055062, CNS-1059235, CNS-1035913, and CNS-1338118. Finally, the work of D. Gunopulos was partially supported by the FP7-ICT project INSIGHT and the General Secretariat for Research and Technology ARISTEIA program project “MMD: Mining Mobility Data”.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Alexios Kotsifakos
    • 1
  • Isak Karlsson
    • 2
  • Panagiotis Papapetrou
    • 2
  • Vassilis Athitsos
    • 1
  • Dimitrios Gunopulos
    • 3
  1. 1.Department of Computer Science and EngineringUniversity of Texas at ArlingtonArlingtonUSA
  2. 2.Department of Computer and Systems SciencesStockholm UniversityStockholmSweden
  3. 3.Department of Informatics and TelecommunicationsNational and Kapodistrian University of AthensAthensGreece

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