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

The final journal paper from my PhD thesis is now available! It’s called “Automatic Chord Estimation from Audio: A Review of the State of the Art”, is currently available on IEEE Explore and will probably be published in the February issue.

Within, we discuss feature extraction, modelling techniques, training and datasets, evaluation strategies (including MIREX, the annual benchmarking evaluation in why our system outperformed all other systems for two years), and software packages for chord estimation.

I’m particularly pleased with two figures in this paper. The first shows the annual performance of algorithms in the MIREX evaluations, clearly showing a performance plateau and overfitting on the Beatles dataset: as well as the challenges and benefits of an unseen test set (the much-appreciated McGill SALAMI dataset) in 2012.


The second is a ‘visual literature review’, showing breakthroughs in various aspects of the automatic chord estimation research problem chronologically:


It feels particularly good to get this this paper published, as it ties up my thesis work and is a quite in-depth study, comprising an IEEE Overview Article, which are ‘solid technical depth and lasting value and should provide advanced readers with a thorough overview of various fields of interest’ and published at most four times a year in the journal. It also gave us twice the usual page limit! A preprint pdf and bibtex link are available on my publications page.¬†Abstract:

In this overview article, we review research on the task of Automatic Chord Estimation (ACE). The major contribu- tions from the last 14 years of research are summarized, with de- tailed discussions of the following topics: feature extraction, mod- eling strategies, model training and datasets, and evaluation strate- gies. Results from the annual benchmarking evaluation Music In- formation Retrieval Evaluation eXchange (MIREX) are also dis- cussed as well as developments in software implementations and the impact of ACE within MIR. We conclude with possible directions for future research.


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