Interpretable Acoustic Representation Learning on Breathing and Speech Signals for COVID-19 Detection

Dutta, D and Bhattacharya, D and Ganapathy, S and Poorjam, AH and Mittal, D and Singh, M (2022) Interpretable Acoustic Representation Learning on Breathing and Speech Signals for COVID-19 Detection. In: 23rd Annual Conference of the International Speech Communication Association, INTERSPEECH 2022, 18 - 22 September 2022, Incheon, pp. 2863-2867.

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Abstract

In this paper, we describe an approach for representation learning of audio signals for the task of COVID-19 detection. The raw audio samples are processed with a bank of 1-D convolutional filters that are parameterized as cosine modulated Gaussian functions. The choice of these kernels allows the interpretation of the filterbanks as smooth band-pass filters. The filtered outputs are pooled, log-compressed and used in a self-attention based relevance weighting mechanism. The relevance weighting emphasizes the key regions of the time-frequency decomposition that are important for the downstream task. The subsequent layers of the model consist of a recurrent architecture and the models are trained for a COVID-19 detection task. In our experiments on the Coswara data set, we show that the proposed model achieves significant performance improvements over the baseline system as well as other representation learning approaches. Further, the approach proposed is shown to be uniformly applicable for speech and breathing signals and for transfer learning from a larger data set.

Item Type:	Conference Paper
Publication:	Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publisher:	International Speech Communication Association
Additional Information:	The copyright for this article belongs to International Speech Communication Association.
Keywords:	Audio signal processing; Filter banks; Learning systems; Speech communication, Audio samples; Audio signal; Breathing signals; Cosine-modulated; COVID-19 diagnose; Learnable filterbank; Parameterized; Representation learning; Self-supervised learning; Speech signals, COVID-19
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	10 Nov 2022 06:09
Last Modified:	10 Nov 2022 06:09
URI:	https://eprints.iisc.ac.in/id/eprint/77851

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