Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/1754
Title: Music Deep Learning: Deep Learning Methods for Music Signal Processing—A Review of the State-of-the-Art
Authors: Moysis, Lazaros
Iliadis, Lazaros Alexios
Sotiroudis, Sotirios P.
Boursianis, Achilles D.
Papadopoulou, Maria S.
Kokkinidis, Konstantinos-Iraklis D.
Volos, Christos
Sarigiannidis, Panagiotis
Nikolaidis, Spyridon
Goudos, Sotirios K.
Type: Article
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: Deep learning
Machine learning
Music signal processing
Music generation
Neural networks
Music information retrieval
Issue Date: 2023
Source: IEEE Access
Volume: 11
First Page: 17031
Last Page: 17052
Abstract: The discipline of Deep Learning has been recognized for its strong computational tools, which have been extensively used in data and signal processing, with innumerable promising results. Among the many commercial applications of Deep Learning, Music Signal Processing has received an increasing amount of attention over the last decade. This work reviews the most recent developments of Deep Learning in Music signal processing. Two main applications that are discussed are Music Information Retrieval, which spans a plethora of applications, and Music Generation, which can fit a range of musical styles. After a review of both topics, several emerging directions are identified for future research.
URI: https://doi.org/10.1109/ACCESS.2023.3244620
https://ruomo.lib.uom.gr/handle/7000/1754
ISSN: 2169-3536
Other Identifiers: 10.1109/ACCESS.2023.3244620
Appears in Collections:Department of Applied Informatics



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