Music Deep Learning: A Survey on Deep Learning Methods for Music Processing

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Music Deep Learning: A Survey on Deep Learning Methods for Music Processing

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  • June 8, 2022
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Lazaros Alexios Iliadis, Sotirios P Sotiroudis, Kostas Kokkinidis, Panagiotis Sarigiannidis, Spiridon Nikolaidis, Sotirios K Goudos: Music Deep Learning: A Survey on Deep Learning Methods for Music Processing. 2022 11th International Conference on Modern Circuits and Systems Technologies (MOCAST), 2022, ISBN: 978-1-6654-6717-9.

Abstract

Deep Learning has emerged as a powerful set of computational methods achieving great results in a variety of different tasks. Music signal processing, a field with rich commercial applications, seems to benefit too from this data-driven approach. In this paper a review of the state of the art Deep Learning methods applied on music signal processing is provided. A special focus is given in music information retrieval and music generation. In addition, possible future research directions are discussed.

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@conference{9837541,
title = {Music Deep Learning: A Survey on Deep Learning Methods for Music Processing},
author = {Lazaros Alexios Iliadis and Sotirios P Sotiroudis and Kostas Kokkinidis and Panagiotis Sarigiannidis and Spiridon Nikolaidis and Sotirios K Goudos},
url = {https://www.researchgate.net/publication/333014972_Deep_Learning_Techniques_for_Music_Generation_-_A_Survey},
doi = {10.1109/MOCAST54814.2022.9837541},
isbn = {978-1-6654-6717-9},
year  = {2022},
date = {2022-06-08},
booktitle = {2022 11th International Conference on Modern Circuits and Systems Technologies (MOCAST)},
pages = {1-4},
abstract = {Deep Learning has emerged as a powerful set of computational methods achieving great results in a variety of different tasks. Music signal processing, a field with rich commercial applications, seems to benefit too from this data-driven approach. In this paper a review of the state of the art Deep Learning methods applied on music signal processing is provided. A special focus is given in music information retrieval and music generation. In addition, possible future research directions are discussed.},
keywords = {Deep Learning, Music Generation, Music Information Retrieval, Music Signal Processing},
pubstate = {published},
tppubtype = {conference}
}
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