A supervised classification approach for note tracking in polyphonic piano transcription

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Title: A supervised classification approach for note tracking in polyphonic piano transcription
Authors: Valero Mas, José Javier | Benetos, Emmanouil | Iñesta, José M.
Research Group/s: Reconocimiento de Formas e Inteligencia Artificial
Center, Department or Service: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos
Keywords: Note tracking | Polyphonic piano transcription | Onset detection | Supervised classification | Machine learning | Audio analysis
Knowledge Area: Lenguajes y Sistemas Informáticos
Issue Date: 26-Mar-2018
Publisher: Routledge
Citation: Journal of New Music Research. 2018, 47(3): 249-263. doi:10.1080/09298215.2018.1451546
Abstract: In the field of Automatic Music Transcription, note tracking systems constitute a key process in the overall success of the task as they compute the expected note-level abstraction out of a frame-based pitch activation representation. Despite its relevance, note tracking is most commonly performed using a set of hand-crafted rules adjusted in a manual fashion for the data at issue. In this regard, the present work introduces an approach based on machine learning, and more precisely supervised classification, that aims at automatically inferring such policies for the case of piano music. The idea is to segment each pitch band of a frame-based pitch activation into single instances which are subsequently classified as active or non-active note events. Results using a comprehensive set of supervised classification strategies on the MAPS piano data-set report its competitiveness against other commonly considered strategies for note tracking as well as an improvement of more than +10% in terms of F-measure when compared to the baseline considered for both frame-level and note-level evaluations.
Sponsor: This research work is partially supported by Universidad de Alicante through the FPU program [UAFPU2014–5883] and the Spanish Ministerio de Economía y Competitividad through project TIMuL [No. TIN2013–48152–C2–1–R, supported by EU FEDER funds]. EB is supported by a UK RAEng Research Fellowship [grant number RF/128].
URI: http://hdl.handle.net/10045/79510
ISSN: 0929-8215 (Print) | 1744-5027 (Online)
DOI: 10.1080/09298215.2018.1451546
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2018 Informa UK Limited, trading as Taylor & Francis Group
Peer Review: si
Publisher version: https://doi.org/10.1080/09298215.2018.1451546
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