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.
Highlights
The estimation of a music recording’s global tempo is a classic Music Information Retrieval (MIR) task
We estimated Φ through an Analysis of Variance (ANOVA) for the datasets ISMIR04 Songs, Hainsworth, GTzan, Ballroom, SMC, RWC, and GiantSteps Tempo (Figure 4, a–g) using scores from five different systems (Davies et al, 2009; Percival and Tzanetakis, 2014; Böck et al, 2015; Schreiber and Müller, 2017, 2018b), closely following the approach described in Salamon and Urbano (2012)
In this article we asked the question whether the task of global tempo estimation is solved yet
Summary
The estimation of a music recording’s global tempo is a classic Music Information Retrieval (MIR) task. Through both the datasets and metrics established in 2004 and for MIREX, we have seen global tempo estimation systems improve and have been able to track their performance.
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