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A temporal-spectral generative adversarial network based end-to-end packet loss concealment for wideband speech transmission.
- Source :
-
Journal of the Acoustical Society of America . Oct2021, Vol. 150 Issue 4, p2577-2588. 12p. - Publication Year :
- 2021
-
Abstract
- Packet loss concealment (PLC) aims to mitigate speech impairments caused by packet losses so as to improve speech perceptual quality. This paper proposes an end-to-end PLC algorithm with a time-frequency hybrid generative adversarial network, which incorporates a dilated residual convolution and the integration of a time-domain discriminator and frequency-domain discriminator into a convolutional encoder-decoder architecture. The dilated residual convolution is employed to aggregate the short-term and long-term context information of lost speech frames through two network receptive fields with different dilation rates, and the integrated time-frequency discriminators are proposed to learn multi-resolution time-frequency features from correctly received speech frames with both time-domain waveform and frequency-domain complex spectrums. Both causal and noncausal strategies are proposed for the packet-loss problem, which can effectively reduce the transitional distortion caused by lost speech frames with a significantly reduced number of training parameters and computational complexity. The experimental results show that the proposed method can achieve better performance in terms of three objective measurements, including the signal-to-noise ratio, perceptual evaluation of speech quality, and short-time objective intelligibility. The results of the subjective listening test further confirm a better performance in the speech perceptual quality. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00014966
- Volume :
- 150
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Journal of the Acoustical Society of America
- Publication Type :
- Academic Journal
- Accession number :
- 153318519
- Full Text :
- https://doi.org/10.1121/10.0006528