Edinburgh Research Explorer

Meter Detection and Alignment of MIDI Performance

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

Related Edinburgh Organisations

Open Access permissions

Open

Documents

  • Download as Adobe PDF

    Rights statement: © Andrew McLeod, Mark Steedman. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: Andrew McLeod, Mark Steedman. “Meter Detection and Alignment of MIDI Performance”, 19th International Society for Music Information Retrieval Conference, Paris, France, 2018.

    Final published version, 326 KB, PDF document

    Licence: Creative Commons: Attribution (CC-BY)

http://ismir2018.ircam.fr/doc/pdfs/136_Paper.pdf
http://ismir2018.ircam.fr/pages/events-main-program.html
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 Sep 201827 Sep 2018
http://ismir2018.ircam.fr/

Conference

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

Abstract

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.

Event

19th International Society for Music Information Retrieval Conference

23/09/1827/09/18

Paris, France

Event: Conference

ID: 81559559