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This paper tackles post-hoc interpretability for audio processing networks.
Auditory nonlinearity
JL Goldstein · 1967
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Why are speech spectrograms hard to read?
AM Liberman, Franklin S Cooper, Donald P Shankweiler, and Michael Studdert-Kennedy · 1968
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Algorithms for Non-negative Matrix Factorization
Daniel Lee and H. Sebastian Seung · 2001
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Non-negative matrix factor deconvolution; extraction of multiple sound sources from monophonic inputs
Paris Smaragdis · 2004
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Blind signal decompositions for automatic transcription of polyphonic music: NMF and K-SVD on the benchmark
Nancy Bertin, Roland Badeau, and Gaël Richard · 2007
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Visualizing high-dimensional data using t-SNE
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Speech denoising using nonnegative matrix factorization with priors
Kevin W Wilson, Bhiksha Raj, Paris Smaragdis, and Ajay Divakaran · 2008
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Automatic relevance determination in nonnegative matrix factorization with the/spl beta/-divergence
Vincent YF Tan and Cédric Févotte · 2012
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Real-time transcription and separation of drum recordings based on NMF decomposition
Christian Dittmar and Daniel Gärtner · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
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ESC: Dataset for environmental sound classification
Karol J Piczak · 2015
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Generating visual explanations
Lisa Anne Hendricks, Zeynep Akata, Marcus Rohrbach, Jeff Donahue, Bernt Schiele, and Trevor Darrell · 2016
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Visualizing deep convolutional neural networks using natural pre-images
Aravindh Mahendran and Andrea Vedaldi · 2016
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Why should I trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Feature learning with matrix factorization applied to acoustic scene classification
Victor Bisot, Romain Serizel, Slim Essid, and Gaël Richard · 2017
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Audio set: An ontology and human-labeled dataset for audio events
Jort F Gemmeke, Daniel PW Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R Channing Moore, Manoj Plakal, and Marvin Ritter · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 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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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Deep recurrent NMF for speech separation by unfolding iterative thresholding
Scott Wisdom, Thomas Powers, James Pitton, and Les Atlas · 2017
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Towards robust interpretability with self-explaining neural networks
David Alvarez-Melis and Tommi Jaakkola · 2018
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Nonnegative matrix factorization
Roland Badeau and Tuomas Virtanen · 2018
SONYC urban sound tagging (SONYC-UST): A multilabel dataset from an urban acoustic sensor network
Mark Cartwright, Ana Elisa Mendez Mendez, Jason Cramer, Vincent Lostanlen, Graham Dove, Ho-Hsiang Wu, Justin Salamon, Oded Nov, and Juan Bello · 2019
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Towards automatic concept-based explanations
Amirata Ghorbani, James Wexler, James Y Zou, and Been Kim · 2019
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Understanding and visualizing raw waveform-based CNNs
Hannah Muckenhirn, Vinayak Abrol, Mathew Magimai-Doss, and Sébastien Marcel · 2019
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Identify, locate and separate: Audio-visual object extraction in large video collections using weak supervision
Sanjeel Parekh, Alexey Ozerov, Slim Essid, Ngoc QK Duong, Patrick Pérez, and Gaël Richard · 2019
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Restricting the flow: Information bottlenecks for attribution
Karl Schulz, Leon Sixt, Federico Tombari, and Tim Landgraf · 2019
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Interpreting and explaining deep neural networks for classification of audio signals
Sören Becker, Marcel Ackermann, Sebastian Lapuschkin, Klaus-Robert Müller, and Wojciech Samek · 2018
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Single-channel audio source separation with nmf: divergences, constraints and algorithms
Cédric Févotte, Emmanuel Vincent, and Alexey Ozerov · 2018
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Learning to separate object sounds by watching unlabeled video
Ruohan Gao, Rogerio Feris, and Kristen Grauman · 2018
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Explaining explanations: An overview of interpretability of machine learning
Leilani H Gilpin, David Bau, Ben Z Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal · 2018
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Attention-based deep multiple instance learning
Maximilian Ilse, Jakub Tomczak, and Max Welling · 2018
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, et al · 2018
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Minz Won, Sanghyuk Chun, and Xavier Serra · 2019
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On concept-based explanations in deep neural networks
Chih-Kuan Yeh, Been Kim, Sercan O Arik, Chun-Liang Li, Pradeep Ravikumar, and Tomas Pfister · 2019
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CRNNs for urban sound tagging with spatiotemporal context
Augustin Arnault and Nicolas Riche · 2020
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SONYC-UST-V2: An urban sound tagging dataset with spatiotemporal context
Mark Cartwright, Jason Cramer, Ana Elisa Mendez Mendez, Yu Wang, Ho-Hsiang Wu, Vincent Lostanlen, Magdalena Fuentes, Graham Dove, Charlie Mydlarz, Justin Salamon, et al · 2020
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audiolime: Listenable explanations using source separation
Verena Haunschmid, Ethan Manilow, and Gerhard Widmer · 2020
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Reliable local explanations for machine listening
Saumitra Mishra, Emmanouil Benetos, Bob LT Sturm, and Simon Dixon · 2020
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What went wrong and when? Instance-wise feature importance for time-series black-box models
Sana Tonekaboni, Shalmali Joshi, Kieran Campbell, David K Duvenaud, and Anna Goldenberg · 2020
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Explaining a black-box by using a deep variational information bottleneck approach
Seojin Bang, Pengtao Xie, Heewook Lee, Wei Wu, and Eric Xing · 2021
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Tracing back music emotion predictions to sound sources and intuitive perceptual qualities
Shreyan Chowdhury, Verena Praher, and Gerhard Widmer · 2021
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A Framework to Learn with Interpretation
Jayneel Parekh, Pavlo Mozharovskyi, and Florence d’Alché Buc · 2021
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Deep learning for audio and music
Geoffroy Peeters and Gaël Richard · 2021
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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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