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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We have requested this dataset, but unfortunately it was no longer provided by the creators.
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Acknowledgements
This work has been supported by the German Research Foundation (AB 675/2-1, MU 2686/11-1). The International Audio Laboratories Erlangen are a joint institution of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Fraunhofer Institut für Integrierte Schaltungen IIS.
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Nowakowski, M., Weiß, C., Abeßer, J. (2021). Towards Deep Learning Strategies for Transcribing Electroacoustic Music. In: Kronland-Martinet, R., Ystad, S., Aramaki, M. (eds) Perception, Representations, Image, Sound, Music. CMMR 2019. Lecture Notes in Computer Science(), vol 12631. Springer, Cham. https://doi.org/10.1007/978-3-030-70210-6_3
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