Packet loss concealment based on extrapolation of speech waveform

Chen, Juin-Hwey

doi:10.1109/icassp.2009.4960537

Cited by 15 publications

(5 citation statements)

References 9 publications

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“…The lookahead technique allows the system to peek at one frame in the future; it earns a big improvement in the speech quality at the cost of a slightly increased latency. We note that lookahead for PLC was also proposed in [17]; our contribution here is showing that lookahead can be easily integrated into a deep learning framework. The masked training technique deals with the scenario of high packet loss rates.…”

Section: Introductionmentioning

confidence: 93%

A Time-Domain Convolutional Recurrent Network for Packet Loss Concealment

Lin

Wang

Kalgaonkar

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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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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Section: Introductionmentioning

confidence: 93%

A Time-Domain Convolutional Recurrent Network for Packet Loss Concealment

Lin

Wang

Kalgaonkar

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Model-based PLC methods rely on a specific model for patching signal gaps. A very popular choice is the Auto-Regressive model combined with Least Squares minimization (LSAR) [78]. Such methods, however, are only effective for filling the gap left by very short individual packets.…”

Section: F Packet Loss Concealment Techniquesmentioning

confidence: 99%

An Overview on Networked Music Performance Technologies

Chafe²,

et al. 2016

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Networked music performance (NMP) is a potential game changer among Internet applications, as it aims at revolutionizing the traditional concept of musical interaction by enabling remote musicians to interact and perform together through a telecommunication network. Ensuring realistic performance conditions, however, constitutes a significant engineering challenge due to the extremely strict requirements in terms of network delay and audio quality, which are needed to maintain a stable tempo, a satisfying synchronicity between performers and, more generally, a high-quality interaction experience. In this paper, we offer a review of the psycho-perceptual studies conducted in the past decade, aimed at identifying latency tolerance thresholds for synchronous real-time musical performance. We also provide an overview of hardware/software enabling technologies for NMP, with a particular emphasis on system architecture paradigms, networking configurations, and applications to real use cases.

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“…They concentrate attentions on digital speech transmission and work well for speech audio signals but yield poor results for audio signals with music. In the bibliography, these techniques can be classified into repetition methods, interpolation/extrapolation methods and more sophisticated regeneration methods based on a speech model [5][6][7][8]. There are also some PLC algorithms proposed for modified discrete cosine transform (MDCT) based codecs [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%