Music Tempo Estimation: Are We Done Yet?

Hendrik Schreiber*, Julián Urbano, Meinard Müller

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)
155 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)111–125
Number of pages15
JournalTransactions of the International Society on Music Information Retrieval
Volume3
Issue number1
DOIs
Publication statusPublished - 2020

Keywords

  • Evaluation
  • Tempo Estimation
  • Metric
  • Dataset

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