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

46 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)556-575
Number of pages20
JournalIEEE Transactions on Audio, Speech, and Language Processing
Volume22
Issue number2
DOIs
Publication statusPublished - 1 Jan 2014

Keywords

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

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