Meter Detection and Alignment of MIDI Performance

Andrew Mcleod, Mark Steedman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Metrical alignment is an integral part of any complete automatic music transcription (AMT) system. In this paper, we present an HMM for both detecting the metrical structure of given live performance MIDI data, and aligning that structure with the underlying notes. The model takes as input only a list of the notes present in a performance, and labels bars, beats, and sub beats in time. We also present an incremental algorithm which can perform inference on the model efficiently using a modified Viterbi search. We propose a new metric designed for the task, and using it, we show that our model achieves state-of-the-art performance on a corpus of metronomically aligned MIDI data, as well as a second corpus of live performance MIDI data. The code for the model described in this paper is available at https://www.github.com/apmcleod/met-align.
Original languageEnglish
Title of host publicationProceedings of the 19th International Society for Music Information Retrieval Conference, ISMIR 2018
Subtitle of host publicationParis, France, September 23-27, 2018
EditorsEmilia Gómez, Xiao Hu, Eric Humphrey
Pages113-119
Number of pages7
Publication statusPublished - 20 Nov 2018
Event19th International Society for Music Information Retrieval Conference - Paris, France
Duration: 23 Sept 201827 Sept 2018
http://ismir2018.ircam.fr/

Conference

Conference19th International Society for Music Information Retrieval Conference
Abbreviated titleISMIR 2019
Country/TerritoryFrance
CityParis
Period23/09/1827/09/18
Internet address

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