Music Tempo Estimation: Are We Done Yet?
Hendrik Schreiber, Julián Urbano, Meinard Müller
2020Transactions of the International Society for Music Information Retrieval14 citationsDOIOpen Access PDF
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.
Topics & Concepts
Computer scienceEstimationData scienceDomain (mathematical analysis)Public domainCode (set theory)Artificial intelligenceMachine learningData miningSystems engineeringEngineeringGeographySet (abstract data type)MathematicsMathematical analysisArchaeologyProgramming languageMusic and Audio ProcessingMusic Technology and Sound StudiesSpeech and Audio Processing