Automatic chord estimation from audio: a review of the state of the art
- Author
- Matt McVicar, Raul Santos-Rodriguez, Yizhao Ni and Tijl De Bie (UGent)
- Organization
- Abstract
- In this overview article, we review research on the task of Automatic Chord Estimation (ACE). The major contributions from the last 14 years of research are summarized, with detailed discussions of the following topics: feature extraction, modeling strategies, model training and datasets, and evaluation strategies. Results from the annual benchmarking evaluation Music Information Retrieval Evaluation eXchange (MIREX) are also discussed as well as developments in software implementations and the impact of ACE within MIR. We conclude with possible directions for future research.
- Keywords
- machine learning, supervised learning, Music information retrieval, SYSTEM, TRANSFORM, TRANSCRIPTION, RECOGNITION, KEY, MUSIC, HIDDEN MARKOV-MODELS, BEAT TRACKING, knowledge based systems, expert systems
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-6936363
- MLA
- McVicar, Matt, et al. “Automatic Chord Estimation from Audio: A Review of the State of the Art.” IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, vol. 22, no. 2, 2014, pp. 556–75, doi:10.1109/TASLP.2013.2294580.
- APA
- McVicar, M., Santos-Rodriguez, R., Ni, Y., & De Bie, T. (2014). Automatic chord estimation from audio: a review of the state of the art. IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 22(2), 556–575. https://doi.org/10.1109/TASLP.2013.2294580
- Chicago author-date
- McVicar, Matt, Raul Santos-Rodriguez, Yizhao Ni, and Tijl De Bie. 2014. “Automatic Chord Estimation from Audio: A Review of the State of the Art.” IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 22 (2): 556–75. https://doi.org/10.1109/TASLP.2013.2294580.
- Chicago author-date (all authors)
- McVicar, Matt, Raul Santos-Rodriguez, Yizhao Ni, and Tijl De Bie. 2014. “Automatic Chord Estimation from Audio: A Review of the State of the Art.” IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 22 (2): 556–575. doi:10.1109/TASLP.2013.2294580.
- Vancouver
- 1.McVicar M, Santos-Rodriguez R, Ni Y, De Bie T. Automatic chord estimation from audio: a review of the state of the art. IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING. 2014;22(2):556–75.
- IEEE
- [1]M. McVicar, R. Santos-Rodriguez, Y. Ni, and T. De Bie, “Automatic chord estimation from audio: a review of the state of the art,” IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, vol. 22, no. 2, pp. 556–575, 2014.
@article{6936363,
abstract = {{In this overview article, we review research on the task of Automatic Chord Estimation (ACE). The major contributions from the last 14 years of research are summarized, with detailed discussions of the following topics: feature extraction, modeling strategies, model training and datasets, and evaluation strategies. Results from the annual benchmarking evaluation Music Information Retrieval Evaluation eXchange (MIREX) are also discussed as well as developments in software implementations and the impact of ACE within MIR. We conclude with possible directions for future research.}},
author = {{McVicar, Matt and Santos-Rodriguez, Raul and Ni, Yizhao and De Bie, Tijl}},
issn = {{2329-9290}},
journal = {{IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING}},
keywords = {{machine learning,supervised learning,Music information retrieval,SYSTEM,TRANSFORM,TRANSCRIPTION,RECOGNITION,KEY,MUSIC,HIDDEN MARKOV-MODELS,BEAT TRACKING,knowledge based systems,expert systems}},
language = {{eng}},
number = {{2}},
pages = {{556--575}},
title = {{Automatic chord estimation from audio: a review of the state of the art}},
url = {{http://dx.doi.org/10.1109/TASLP.2013.2294580}},
volume = {{22}},
year = {{2014}},
}
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