Music Tempo Estimation: Are We Done Yet?

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..

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:3

Enthalten in:

Transactions of the International Society for Music Information Retrieval - 3(2020), 1

Sprache:

Englisch

Beteiligte Personen:

Hendrik Schreiber [VerfasserIn]
Julián Urbano [VerfasserIn]
Meinard Müller [VerfasserIn]

Links:

doi.org [kostenfrei]
doaj.org [kostenfrei]
transactions.ismir.net [kostenfrei]
Journal toc [kostenfrei]

Themen:

Dataset
Evaluation
Information technology
Metric
Music
Tempo estimation

doi:

10.5334/tismir.43

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

DOAJ051476770