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MDMS: Music Data Matching System for Query Variant Retrieval

Published: 17 October 2021 Publication History

Abstract

The distribution of royalty fees to music right holders is slow and inefficient due to the lack of automation in music recognition and music licensing processes. The challenge for an improved system is to recognise different versions of a music such as remix or cover versions, leading to clear assessment and unique identification of each music work. Through our music data matching system called MDMS, we query many indexed and stored music pieces with a small part of a music piece. The system retrieves the closest stored variant of the input query by using music fingerprints of the underlying melody together with signal processing techniques. Tailored indices based on fingerprint hashes accelerate processing across a large corpus of stored music. Results are found even if the stored versions vary from the query song in terms of one or more music features --- tempo, key/mode, presence of instruments/vocals, and singer --- and the differences are highlighted in the output.

Supplementary Material

MP4 File (de3186.mp4)
Supplemental video
MP4 File (MDMS_ACMMM2021Demo_3186_VideoFigure.mp4)
Short description video for MDMS, a music data matching system for query variant retrieval. The video contains the explanations of the functionalities of MDMS. It also includes the differences of MDMS with the existing systems and highlights the novelties. MDMS retrieves the closest match of an input audio song from the database even when the exact version is not stored, and shows the notable differences between the input and the output versions. Finally, a demonstration of the web application is shown with two of the different use cases.

References

[1]
P. Mandl et al. Die Verwertung von Online-Musiknutzungen -- Herausforderungen fuer die IT, pages 126--138. 2016.
[2]
T. Ingham. Over 60,000 tracks are now uploaded to spotify every day. That's nearly one per second, 2021.
[3]
European Parliament. Liability of online service providers for copyrighted content -- regulatory action needed?, 2017.
[4]
R. J. McNab et al. Towards the digital music library: Tune retrieval from acoustic input. In ACM International Conference on Digital Libraries, pages 11--18, 1996.
[5]
Y. Zhu et al. Pitch tracking and melody slope matching for song retrieval. In Advances in Multimedia Information Processing, pages 530--537, 2001.
[6]
J.R. Jang and H. Lee. Hierarchical filtering method for content-based music retrieval via acoustic input. In ACM Multimedia, pages 401--410, 2001.
[7]
Y. Zhu and D. Shasha. Warping indexes with envelope transforms for query by humming. In ACM SIGMOD, pages 181--192, 2003.
[8]
A. L. Wang. An industrial-strength audio search algorithm. In ISMIR, pages 7--13, 2003.
[9]
W. Drevo. Audio fingerprinting with python and numpy, 2013.
[10]
R. Hennequin et al. Spleeter: a fast and efficient music source separation tool with pre-trained models. 2020.

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Published In

cover image ACM Conferences
MM '21: Proceedings of the 29th ACM International Conference on Multimedia
October 2021
5796 pages
ISBN:9781450386517
DOI:10.1145/3474085
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2021

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Author Tags

  1. music content analysis
  2. music copyright matching
  3. music database
  4. music feature extraction
  5. music matching
  6. music variant comparison
  7. singer classification
  8. top-k retrieval

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  • Demonstration

Funding Sources

  • Bayerisches Staatsministerium fur Wirtschaft Landesentwicklung und Energie

Conference

MM '21
Sponsor:
MM '21: ACM Multimedia Conference
October 20 - 24, 2021
Virtual Event, China

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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