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

被引：39

作者：

McVicar, Matt ^{[1
]}

Santos-Rodriguez, Raul ^{[1
]}

Ni, Yizhao ^{[1
]}

De Bie, Tijl ^{[1
]}

机构：

[1] Univ Bristol, Dept Engn Math, Intelligent Syst Lab, Bristol BS8 1UB, Avon, England

来源：

IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING | 2014年 / 22卷 / 02期

关键词：

Music information retrieval; machine learning; supervised learning; knowledge based systems; expert systems; KEY; TRANSCRIPTION; TRACKING; SYSTEM; MODEL;

D O I：

10.1109/TASLP.2013.2294580

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

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.

引用

页码：556 / 575

页数：20

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