Automatic Chord Estimation from Audio: A Review of the State of the Art

Author

McVicar, Matt ; Santos-Rodriguez, R. ; Yizhao Ni ; Tijl De Bie

Author_Institution

Dept. of Eng. Math., Univ. of Bristol, Bristol, UK

Volume

22

Issue

2

fYear

2014

fDate

Feb. 2014

Firstpage

556

Lastpage

575

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

feature extraction; information retrieval; learning (artificial intelligence); music; ACE; MIR; MIREX; audio signal; automatic chord estimation; feature extraction; model training; music information retrieval evaluation exchange; Accuracy; Feature extraction; Harmonic analysis; Spectrogram; Time-frequency analysis; Tuning; Vectors; Music information retrieval; expert systems; knowledge based systems; machine learning; supervised learning;

fLanguage

English

Journal_Title

Audio, Speech, and Language Processing, IEEE/ACM Transactions on

Publisher

ieee

ISSN

2329-9290

Type

jour

DOI

10.1109/TASLP.2013.2294580

Filename

6705583