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The Deep Learning Revolution in MIR: The Pros and Cons, the Needs and the Challenges Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Towards Deep Learning Strategies for Transcribing Electroacoustic Music

Authors : Matthias Nowakowski, Christof Weiß, Jakob Abeßer

Published in: Perception, Representations, Image, Sound, Music

Publisher: Springer International Publishing

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Abstract

Electroacoustic music is experienced primarily through auditory perception, as it is not usually based on a prescriptive score. For the analysis of such pieces, transcriptions are sometimes created to illustrate events and processes graphically in a readily comprehensible way. These are usually based on the spectrogram of the recording. Although the manual generation of transcriptions is often time-consuming, they provide a useful starting point for any person who has interest in a work. Deep-learning algorithms that learn to recognize characteristic spectral patterns using supervised learning represent a promising technology to automatize this task. This paper investigates and explores the labeling of sound objects in electroacoustic music recordings. We test several neural-network architectures that enable classification of sound objects using musicological and signal-processing methods. We also show future perspectives how our results can be improved and applied to a new gradient-based visualization approach.

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Footnotes
4
We have requested this dataset, but unfortunately it was no longer provided by the creators.
 
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Metadata
Title
Towards Deep Learning Strategies for Transcribing Electroacoustic Music
Authors
Matthias Nowakowski
Christof Weiß
Jakob Abeßer
Copyright Year
2021
Book
Perception, Representations, Image, Sound, Music

Print ISBN: 978-3-030-70209-0

Electronic ISBN: 978-3-030-70210-6

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-70210-6_3