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Towards Deep Learning Strategies for Transcribing Electroacoustic Music

 
: Nowakowski, M.; Weiß, C.; Abeßer, J.

:

Kronland-Martinet, R.:
Perception, Representations, Image, Sound, Music : 14th International Symposium, CMMR 2019, Marseille, France, October 14-18, 2019, Revised Selected Papers
Cham: Springer Nature, 2021 (Lecture Notes in Computer Science 12631)
ISBN: 978-3-030-70209-0 (Print)
ISBN: 978-3-030-70210-6 (Online)
ISBN: 978-3-030-70211-3
pp.48-59
International Symposium on Computer Music Multidisciplinary Research (CMMR) <14, 2019, Marseille>
English
Conference Paper
Fraunhofer IDMT ()

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.

: http://publica.fraunhofer.de/documents/N-637747.html