Music Tempo Estimation: Are We Done Yet?
Schreiber H, Urbano J, Müller M (2020)
Publication Type: Journal article
Publication year: 2020
Journal
Book Volume: 3
Pages Range: 111
Issue: 1
DOI: 10.5334/tismir.43
Abstract
With the advent of deep learning, global tempo estimation accuracy has reached a new peak, which presents a great opportunity to evaluate our evaluation practices. In this article, we discuss presumed and actual applications, the pros and cons of commonly used metrics, and the suitability of popular datasets. To guide future research, we present results of a survey among domain experts that investigates today’s applications, their requirements, and the usefulness of currently employed metrics. To aid future evaluations, we present a public repository containing evaluation code as well as estimates by many different systems and different ground truths for popular datasets.
Authors with CRIS profile
Hendrik Schreiber
International Audio Laboratories Erlangen (AudioLabs)
Meinard Müller
Lehrstuhl für Semantische Audiosignalverarbeitung (AudioLabs)
Involved external institutions
Netherlands (NL)How to cite
APA:
Schreiber, H., Urbano, J., & Müller, M. (2020). Music Tempo Estimation: Are We Done Yet? Transactions of the International Society for Music Information Retrieval, 3, 111. https://doi.org/10.5334/tismir.43
MLA:
Schreiber, Hendrik, Julián Urbano, and Meinard Müller. "Music Tempo Estimation: Are We Done Yet?" Transactions of the International Society for Music Information Retrieval 3 (2020): 111.
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