Improving Perceived Tempo Estimation by Statistical Modeling of Higher-Level Musical Descriptors

Chen CW, Cremer M, Lee K, DiMaria P, Wu HH. Improving Perceived Tempo Estimation by Statistical Modeling of Higher-Level Musical Descriptors. In: AES Convention 126. Audio Engineering Society; 2009. Paper 7777. Available from: https://aes.org/publications/elibrary-page/?id=14973

@inproceedings{Chen2009_14973,
  author = {Chen, Ching-Wei and Cremer, Markus and Lee, Kyogu and DiMaria, Peter and Wu, Ho-Hsiang},
  title = {{Improving Perceived Tempo Estimation by Statistical Modeling of Higher-Level Musical Descriptors}},
  booktitle = {AES Convention 126},
  note = {Paper 7777},
  year = {2009},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=14973}
}

TY  - CPAPER
TI  - Improving Perceived Tempo Estimation by Statistical Modeling of Higher-Level Musical Descriptors
AU  - Chen, Ching-Wei
AU  - Cremer, Markus
AU  - Lee, Kyogu
AU  - DiMaria, Peter
AU  - Wu, Ho-Hsiang
T2  - AES Convention 126
M1  - Paper 7777
PY  - 2009
DA  - 2009/05/06
UR  - https://aes.org/publications/elibrary-page/?id=14973
PB  - Audio Engineering Society
LA  - en
AB  - Conventional tempo estimation algorithms generally work by detecting significant audio events and finding periodicities of repetitive patterns in an audio signal. However, human perception of tempo is subjective, and relies on a far richer set of information, causing many tempo estimation algorithms to suffer from octave errors, or “double/half-time” confusion. In this paper, we propose a system that uses higher-level musical descriptors such as mood to train a statistical model of perceived tempo classes, which can then used to correct the estimate from a conventional tempo estimation algorithm. Our experimental results show reliable classification of perceived tempo class, as well as a significant reduction of octave errors when applied to an array of available tempo estimation algorithms.
ER  -