TY - GEN
T1 - Tempo vs. Pitch
T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
AU - Morais, Giovana
AU - Davies, Matthew E.P.
AU - Queiroz, Marcelo
AU - Fuentes, Magdalena
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Self-supervision methods learn representations by solving pretext tasks that do not require human-generated labels, alleviating the need for time-consuming annotations. These methods have been applied in computer vision, natural language processing, environmental sound analysis, and recently in music information retrieval, e.g. for pitch estimation. Particularly in the context of music, there are few insights about the fragility of these models regarding different distributions of data, and how they could be mitigated. In this paper, we explore these questions by dissecting a self-supervised model for pitch estimation adapted for tempo estimation via rigorous experimentation with synthetic data. Specifically, we study the relationship between the input representation and data distribution for self-supervised tempo estimation.
AB - Self-supervision methods learn representations by solving pretext tasks that do not require human-generated labels, alleviating the need for time-consuming annotations. These methods have been applied in computer vision, natural language processing, environmental sound analysis, and recently in music information retrieval, e.g. for pitch estimation. Particularly in the context of music, there are few insights about the fragility of these models regarding different distributions of data, and how they could be mitigated. In this paper, we explore these questions by dissecting a self-supervised model for pitch estimation adapted for tempo estimation via rigorous experimentation with synthetic data. Specifically, we study the relationship between the input representation and data distribution for self-supervised tempo estimation.
KW - Self-supervision
KW - tempo estimation
UR - http://www.scopus.com/inward/record.url?scp=85177559014&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85177559014&partnerID=8YFLogxK
U2 - 10.1109/ICASSP49357.2023.10095292
DO - 10.1109/ICASSP49357.2023.10095292
M3 - Conference contribution
AN - SCOPUS:85177559014
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
BT - ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 June 2023 through 10 June 2023
ER -