Time Signature Detection: A Survey

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 19; p. 6494
• Main Authors: Abimbola, Jeremiah, Kostrzewa, Daniel, Kasprowski, Pawel
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.09.2021; MDPI
• Subjects: Algorithms; Automation; Deep Learning; Genre; Information retrieval; measure signature; meter; metre; Music; music information retrieval; Research projects; Review; Signal processing; time signature; metre; deep learning; time signature; signal processing; meter; measure signature; music information retrieval
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21196494

This paper presents a thorough review of methods used in various research articles published in the field of time signature estimation and detection from 2003 to the present. The purpose of this review is to investigate the effectiveness of these methods and how they perform on different types of input signals (audio and MIDI). The results of the research have been divided into two categories: classical and deep learning techniques, and are summarized in order to make suggestions for future study. More than 110 publications from top journals and conferences written in English were reviewed, and each of the research selected was fully examined to demonstrate the feasibility of the approach used, the dataset, and accuracy obtained. Results of the studies analyzed show that, in general, the process of time signature estimation is a difficult one. However, the success of this research area could be an added advantage in a broader area of music genre classification using deep learning techniques. Suggestions for improved estimates and future research projects are also discussed.