An Interpretable Deep Learning Model for Automatic Sound Classification
-
Published:2021-04-02
Issue:7
Volume:10
Page:850
-
ISSN:2079-9292
-
Container-title:Electronics
-
language:en
-
Short-container-title:Electronics
Author:
Zinemanas Pablo1ORCID, Rocamora Martín2ORCID, Miron Marius1ORCID, Font Frederic1ORCID, Serra Xavier1ORCID
Affiliation:
1. Music Technology Group, Universitat Pompeu Fabra, 08018 Barcelona, Spain 2. Facultad de Ingeniería, Universidad de la República, Montevideo 11300, Uruguay
Abstract
Deep learning models have improved cutting-edge technologies in many research areas, but their black-box structure makes it difficult to understand their inner workings and the rationale behind their predictions. This may lead to unintended effects, such as being susceptible to adversarial attacks or the reinforcement of biases. There is still a lack of research in the audio domain, despite the increasing interest in developing deep learning models that provide explanations of their decisions. To reduce this gap, we propose a novel interpretable deep learning model for automatic sound classification, which explains its predictions based on the similarity of the input to a set of learned prototypes in a latent space. We leverage domain knowledge by designing a frequency-dependent similarity measure and by considering different time-frequency resolutions in the feature space. The proposed model achieves results that are comparable to that of the state-of-the-art methods in three different sound classification tasks involving speech, music, and environmental audio. In addition, we present two automatic methods to prune the proposed model that exploit its interpretability. Our system is open source and it is accompanied by a web application for the manual editing of the model, which allows for a human-in-the-loop debugging approach.
Reference84 articles.
1. Deep learning;LeCun;Nature,2015 2. Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press. Available online: http://www.deeplearningbook.org. 3. Pereira, F., Burges, C.J.C., Bottou, L., and Weinberger, K.Q. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 25, Curran Associates, Inc. 4. Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., and Zettlemoyer, L. (2018). Deep Contextualized Word Representations. Volume 1 (Long Papers), Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, New Orleans, LA, USA, 2–7 February 2018, Association for Computational Linguistics. 5. Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups;Hinton;IEEE Signal Process. Mag.,2012
Cited by
42 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|