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Sight to Sound: An End-to-End Approach for Visual Piano Transcription

Abstract:

Automatic music transcription has primarily focused on transcribing audio to a symbolic music representation (e.g. MIDI or sheet music). However, audio-only approaches often struggle with polyphonic instruments and background noise. In contrast, visual information (e.g. a video of an instrument being played) does not have such ambiguities. In this work, we address the problem of transcribing piano music from visual data alone. We propose an end-to-end deep learning framework that learns to au...

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Publication status:
Published
Peer review status:
Reviewed (other)

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Publisher copy:
10.1109/ICASSP40776.2020.9053115

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Brasenose College
Role:
Author
ORCID:
0000-0002-8945-8573
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Name:
Engineering & Physical Sciences Research Council
Grant:
EP/M013774/1
Publisher:
IEEE
Host title:
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Pages:
1838-1842
Publication date:
2020-04-09
Acceptance date:
2020-01-24
Event title:
International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Event location:
Barcelona, Spain
Event website:
https://2020.ieeeicassp.org/
Event start date:
2020-05-04
Event end date:
2020-05-08
DOI:
EISSN:
2379-190X
ISSN:
1520-6149
EISBN:
978-1-5090-6631-5
Language:
English
Keywords:
Pubs id:
1102360
Local pid:
pubs:1102360
Deposit date:
2020-04-29

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