Audio packet loss concealment using spectral motion

Packet loss may affect a wide range of applications that use voice over IP (VoIP), e.g. video conferencing. In this paper, we investigate a time-domain convolutional recurrent network (CRN) for online packet loss concealment. The CRN comprises a convolutional encoder-decoder structure and long short-term memory (LSTM) layers, which have been shown to be suitable for real-time speech enhancement applications. Moreover, we propose lookahead and masked training to further improve the performance of the CRN framework. Experimental results show that the proposed system outperforms a baseline system using only LSTM layers in terms of two objective metrics -perceptual evaluation of speech quality (PESQ) and short-term objective intelligibility (STOI); it also reduces the word error rate (WER) more than the baseline when used as a frontend for speech recognition. The advantage of the proposed system is also verified in a subjective evaluation by the mean opinion score (MOS).

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Concealing Audio Packet Loss Using Frequency- Consistent Generative Adversarial Networks

Chen

2022

2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI)

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Audio packet loss concealment using spectral motion

Cited by 3 publications

References 10 publications

Packet Loss Concealment for Improving Audio Streaming Service

Packet Loss Concealment for Improving Audio Streaming Service

A Time-Domain Convolutional Recurrent Network for Packet Loss Concealment

Concealing Audio Packet Loss Using Frequency- Consistent Generative Adversarial Networks

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