Music Deep Learning: Deep Learning Methods for Music Signal Processing—A Review of the State-of-the-Art-Reference-Cited by-同舟云学术

Music Deep Learning: Deep Learning Methods for Music Signal Processing—A Review of the State-of-the-Art

Published:2023 Issue: Volume:11 Page:17031-17052
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Moysis Lazaros¹^ORCID,Iliadis Lazaros Alexios²^ORCID,Sotiroudis Sotirios P.²^ORCID,Boursianis Achilles D.²^ORCID,Papadopoulou Maria S.²,Kokkinidis Konstantinos-Iraklis D.³,Volos Christos¹^ORCID,Sarigiannidis Panagiotis⁴^ORCID,Nikolaidis Spiridon²^ORCID,Goudos Sotirios K.²^ORCID

Affiliation:

1. Laboratory of Nonlinear Systems-Circuits and Complexity, School of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece

2. ELEDIA@AUTH, School of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece

3. Department of Applied Informatics, University of Macedonia, Thessaloniki, Greece

4. Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani, Greece

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10043650.pdf?arnumber=10043650

Reference288 articles.

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3. Holistic Approaches to Music Genre Classification using Efficient Transfer and Deep Learning Techniques

4. The computational limits of deep learning;thompson;arXiv 2007 05558,2020

5. A deep bidirectional long short-term memory based multi-scale approach for music dynamic emotion prediction

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