TY - GEN
T1 - MDMS
T2 - 29th ACM International Conference on Multimedia, MM 2021
AU - Roy, Rinita
AU - Mayer, Ruben
AU - Jacobsen, Hans Arno
N1 - Publisher Copyright:
© 2021 Owner/Author.
PY - 2021/10/17
Y1 - 2021/10/17
N2 - 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.
AB - 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.
KW - music content analysis
KW - music copyright matching
KW - music database
KW - music feature extraction
KW - music matching
KW - music variant comparison
KW - singer classification
KW - top-k retrieval
UR - http://www.scopus.com/inward/record.url?scp=85119354583&partnerID=8YFLogxK
U2 - 10.1145/3474085.3478551
DO - 10.1145/3474085.3478551
M3 - Conference contribution
AN - SCOPUS:85119354583
T3 - MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia
SP - 2762
EP - 2764
BT - MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia
PB - Association for Computing Machinery, Inc
Y2 - 20 October 2021 through 24 October 2021
ER -