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We present PECMAE, an interpretable model for music audio classification based on prototype learning.
“Representing musical genre: A state of the art,”
Jean-Julien Aucouturier and Francois Pachet, · 2003
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Cory McKay and Ichiro Fujinaga, · 2006
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Alastair J. D. Craft, Geraint A. Wiggins, and Tim Crawford, · 2007
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“A survey of evaluation in music genre recognition,”
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Bob L. Sturm, · 2013
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Sander Dieleman and Benjamin Schrauwen, · 2014
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Vincent Lostanlen and Carmine-Emanuele Cella, · 2016
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Bob L. Sturm, · 2017
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“Local interpretable model-agnostic explanations for music content analysis.,”
Saumitra Mishra, Bob L Sturm, and Simon Dixon, · 2017
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Michaël Defferrard, Kirell Benzi, Pierre Vandergheynst, and Xavier Bresson, · 2017
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“Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions,”
Oscar Li, Hao Liu, Chaofan Chen, and Cynthia Rudin, · 2018
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Taejun Kim, Jongpil Lee, and Juhan Nam, · 2018
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Dmitry Bogdanov, Alastair Porter, Hendrik Schreiber, Julián Urbano, and Sergio Oramas, · 2019
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“Leveraging knowledge bases and parallel annotations for music genre translation,”
Elena V Epure, Anis Khlif, and Romain Hennequin, · 2019
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“An interpretable deep learning model for automatic sound classification,”
Pablo Zinemanas, Martín Rocamora, Marius Miron, Frederic Font, and Xavier Serra, · 2021
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“SoundStream: An end-to-end neural audio codec,”
Neil Zeghidour, Alejandro Luebs, Ahmed Omran, Jan Skoglund, and Marco Tagliasacchi, · 2021
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“Progressive distillation for fast sampling of diffusion models,”
Tim Salimans and Jonathan Ho, · 2021
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Interpretable Machine Learning
Christoph Molnar, · 2022
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“A model you can hear: Audio identification with playable prototypes,”
Romain Loiseau, Baptiste Bouvier, Yan Teytaut, Elliot Vincent, Mathieu Aubry, and Loic Landrieu, · 2022
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Minz Won, Sanghyuk Chun, and Xavier Serra, · 2019
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“Joint time–frequency scattering,”
Joakim Andén, Vincent Lostanlen, and Stéphane Mallat, · 2019
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“Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI,”
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador Garcia, Sergio Gil-Lopez, Daniel Molina, Richard Benjamins, Raja Chatila, and Francisco Herrera, · 2020
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“The deep learning revolution in MIR: The pros and cons, the needs and the challenges,”
Geoffroy Peeters, · 2021
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“On end-to-end white-box adversarial attacks in music information retrieval,”
Katharina Prinz, Arthur Flexer, and Gerhard Widmer, · 2021
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Alexandre Défossez, Jade Copet, Gabriel Synnaeve, and Yossi Adi, · 2022
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“EnCodecMAE: Leveraging neural codecs for universal audio representation learning,”
Leonardo Pepino, Pablo Riera, and Luciana Ferrer, · 2023
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“Transparency in music-generative AI: A systematic literature review,”
Roser Batlle-Roca, Emila Gómez, WeiHsiang Liao, Xavier Serra, and Yuki Mitsufuji, · 2023
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“High-fidelity audio compression with improved RVQGAN,”
Rithesh Kumar, Prem Seetharaman, Alejandro Luebs, Ishaan Kumar, and Kundan Kumar, · 2023
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“Efficient supervised training of audio transformers for music representation learning,”
Pablo Alonso-Jiménez, Xavier Serra, and Dmitry Bogdanov, · 2023
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