COVID-19 Detection from Speech in Noisy Conditions

Liu, Shuo; Mallol-Ragolta, Adria; Schuller, Björn W.

doi:10.1109/icassp49357.2023.10094304

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…Speech has been discovered as a useful biomarker for the detection of COVID-19. The model proposed in [209] is used to denoise speech using conventional U-Net which is then classified for the absence or otherwise of COVID-19. The detection performance is improved by 10% especially under low SNR conditions as compared to the detection system without speech enhancement.…”

Section: B) Health Carementioning

confidence: 99%

A Survey of Audio Enhancement Algorithms for Music, Speech, Bioacoustics, Biomedical, Industrial, and Environmental Sounds by Image U-Net

Gul,

Khan

2023

IEEE Access

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The recent surge in the use of Deep Neural Networks (DNNs) has also made its mark in the field of Audio Enhancement (AE), providing much better quality than the classical methods. Although, there are dedicated audio processing DNNs, yet, many recent models of AE have utilized U-Net: a DNN based on Convolutional Neural Network (CNN), fundamentally developed for image segmentation. It is found that the useful features hidden in the time domain are highlighted when the audio signal is converted to a spectrogram, which can be treated as an image. In this article, we will review the recent work, utilizing U-Nets for different AE applications. Different than other published reviews, this review focuses entirely on AE techniques based on image U-Nets. We will discuss the need for AE, U-Net comparison to other DNNs, the benefits of converting the audio to 2D, input representations that are useful for different AE applications, the architecture of vanilla U-Net and the pre-trained models, variations in vanilla architecture incorporated in different E models, and the state-of-the-art AE algorithms based on U-Net in various applications. Apart from speech and music, this article discusses a wide range of audio signals e.g. environmental, biomedical, bioacoustics, and industrial sounds, not covered collectively in a single article in previously published studies. The article ends with the discussion of colored spectrograms in future AE applications.INDEX TERMS CNNs; image processing deep neural networks; pre-trained networks; spectrogram.;U-Net.

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Section: B) Health Carementioning

confidence: 99%