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MDMS: Music Data Matching System for Query Variant Retrieval

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Published:17 October 2021Publication 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.

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Supplemental Material

de3186.mp4

Supplemental video

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.Google ScholarGoogle Scholar
  2. T. Ingham. Over 60,000 tracks are now uploaded to spotify every day. That's nearly one per second, 2021.Google ScholarGoogle Scholar
  3. European Parliament. Liability of online service providers for copyrighted content -- regulatory action needed?, 2017.Google ScholarGoogle Scholar
  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. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Y. Zhu et al. Pitch tracking and melody slope matching for song retrieval. In Advances in Multimedia Information Processing, pages 530--537, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  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. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Y. Zhu and D. Shasha. Warping indexes with envelope transforms for query by humming. In ACM SIGMOD, pages 181--192, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. A. L. Wang. An industrial-strength audio search algorithm. In ISMIR, pages 7--13, 2003.Google ScholarGoogle Scholar
  9. W. Drevo. Audio fingerprinting with python and numpy, 2013.Google ScholarGoogle Scholar
  10. R. Hennequin et al. Spleeter: a fast and efficient music source separation tool with pre-trained models. 2020.Google ScholarGoogle Scholar

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