Music Deep Learning: A Survey on Deep Learning Methods for Music Processing
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- Ιούνιος 8, 2022
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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.
Περίληψη
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.
Σύνδεσμοι
- https://www.researchgate.net/publication/333014972_Deep_Learning_Techniques_for_[...]
- doi:10.1109/MOCAST54814.2022.9837541
BibTeX (Download)
@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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