Automatic chord estimation from audio: A review of the state of the art

Matt McVicar, Raúl Santos-Rodríguez, Yizhao Ni, Tijl De Bie

Research output: Contribution to journalArticle (Academic Journal)peer-review

63 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)556-575
Number of pages20
JournalIEEE Transactions on Audio, Speech, and Language Processing
Issue number2
Publication statusPublished - 1 Jan 2014


  • Expert systems
  • Knowledge based systems
  • Machine learning
  • Music information retrieval
  • Supervised learning


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