Title :
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
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;
Journal_Title :
Audio, Speech, and Language Processing, IEEE/ACM Transactions on
DOI :
10.1109/TASLP.2013.2294580
