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Roy, Rinita ; Mayer, Ruben ; Jacobsen, Hans-Arno:
MDMS : Music Data Matching System for Query Variant Retrieval.
In:
Proceedings of the 29th ACM International Conference on Multimedia. -
New York
: Association for Computing Machinery
,
2021
. - S. 2762-2764
ISBN 978-1-4503-8651-7
DOI: https://doi.org/10.1145/3474085.3478551
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.
Weitere Angaben
| Publikationsform: | Aufsatz in einem Buch |
|---|---|
| Begutachteter Beitrag: | Ja |
| Institutionen der Universität: | Fakultäten Fakultäten > Fakultät für Mathematik, Physik und Informatik Fakultäten > Fakultät für Mathematik, Physik und Informatik > Institut für Informatik > Lehrstuhl Data Systems Fakultäten > Fakultät für Mathematik, Physik und Informatik > Institut für Informatik > Lehrstuhl Data Systems > Lehrstuhl Data Systems - Univ.-Prof. Dr. Ruben Mayer Fakultäten > Fakultät für Mathematik, Physik und Informatik > Institut für Informatik |
| Titel an der UBT entstanden: | Nein |
| Themengebiete aus DDC: | 000 Informatik,Informationswissenschaft, allgemeine Werke > 004 Informatik |
| Eingestellt am: | 26 Apr 2023 11:48 |
| Letzte Änderung: | 05 Feb 2024 07:34 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/76042 |