Speech quality estimation of voice over internet protocol codec using a packet loss impairment model

In this paper, we propose an improved packet loss concealment (PLC) algorithm for ACELP-based speech coders in order to improve the quality of decoded speech at the receiver. Conventional receiver-based PLC algorithms in the AMR-WB+ speech codec are usually based on speech correlations to reconstruct the decoded speech of the lost frames by using parameter information obtained from the previous or adjacent correctly received frames. However, this approach has difficulties in reconstructing accurate voice signals when consecutive packet loss and bursts of packet loss occur. Thus, we propose a new PLC approach based on data-driven principle to recover the Immittance Spectral Frequen- cy (ISF) coefficients of lost packet, referring to pitch period parameters of adjacent correctly received voice packets. The proposed PLC algorithm is designed for ACELP-based audio codec. We make the performance comparison between the data-driven method and the PLC algorithm currently employed in AMR-WB+ speech codec through the PESQ objective evaluation test and MOS subjective evaluation test. The results prove that our algorithm can achieve better speech recov- ery quality than original PLC algorithm employed in AMR-WB+ speech codec.

In this paper, we present the design of a new packet loss concealment (PLC) algorithm based on Dynamic Forward Error Correction (FEC) to improve speech quality degradation caused by packet losses. It is to be implemented for the standard ITU-T AMR-WD G.722.2 codec. The performance proposed scheme is compared with the original method embedded in the standard ITU-T G.772.2. To evaluate its performance, the perceptual evaluation of speech quality (PESQ) and enhanced modified bark spectral distortion (EMBSD) tests under a variety of packet loss rates are used and the obtained results prove that the proposed PLC based Dynamic FEC is better than the concealment method embedded in the ITU-T G.722.2 for speakers (female and male). The performance measures confirm that our PLC method based Dynamic FEC is superior than the one embedded in the standard ITU-T G.722.2.

In this paper, a Packet Loss Concealment (PLC) algorithm for ITU-G.722.2 speech coder is proposed in order to improve the decoded speech quality under burst packet loss conditions over packet-switched networks. First, we evaluated the repetition of the last received frame method in order to improve the concealment of lost frames. Then, we made a statistical study to evaluate the correlation between parameters values of adjacent frames. Finally, we present a new PLC method for ITU-G.722.2 by proposing an efficient approach to sending the side information to improve concealment of the lost frames and to resynchronize the encoder and decoder. The proposed method significantly improves the robustness of the ITU-G.722.2 codec against packet losses for voice over networks services. Tests in terms of Wideband Perceptual Evaluation of Speech Quality Mean Opinion Score (WB-PESQ MOS) quality measure and MUlti-Stimulus test with Hidden Reference and Anchor (MUSHRA) evaluation confirm the superiority of our proposed method against the PLC method embedded in G.722.2.

This article presents ITU-T G.722 appendix IV which is a packet loss concealment (PLC) algorithm recently standardized by ITU-T for G.722 decoding in the presence of frame erasures. This algorithm is suitable for applications that may encounter frame erasures or packet losses with a special focus on complexity constraints For example, G.722 appendix IV is very suitable for DECT next generation and VoIP using low cost devices. Besides, we also describe some minor algorithmic modifications to G.722 appendix IV that improve subjective quality. We discuss G.722- PLC performance based on formal ITU-T test results as well as additional informal experiments.

A class of packet loss concealment algorithms for speech coding is presented. It generates the replacement waveform for the lost frame by direct extrapolation of the past speech waveform, with or without look-ahead. The ITU-T G.722 Appendix III standard is based on it. When a future frame is unavailable (without look-ahead), the PLC algorithm gives significantly better speech quality than G.711 Appendix I - by about 0.2 PESQ for high packet loss rates. When a future frame is available (with look-ahead), the PLC algorithm uses the decoded speech waveform in the future frame to guide the pitch contour of waveform extrapolation during the lost frame such that the extrapolated waveform is phase-aligned with the decoded waveform after the packet loss. This technique further improved PESQ by another 0.2 for high packet loss rates.

Packet loss concealment (PLC) at a receiver has a substantial effect on the speech quality in Voice over IP. Most conventional PLC systems have largely relied upon variations of signal repetition and overlap-add interpolation which can produce speech signals that do not follow the larger overall statistical trends. In this paper, we demonstrate how Hidden Markov Models can be utilized to effect PLC based on statistical signal processing. In particular, we show how HMM-based PLC yields conditional density functions that can be utilized by various statistical estimation methods that produce signal parameter estimates that produce more natural variation than conventional PLC methods, thereby providing much better speech quality.

In this paper, we compare air‐conducted (AC) speech with bone‐conducted (BC) speech for the purpose of utilizing them in packet loss concealment (PLC) for speech quality of voice over internet protocol (VoIP). Instead of the autocorrelation method of linear prediction (LP), which was utilized in the conventional PLC techniques, we employ the modified covariance (MC) method. The MC method provides accurate LP estimation from short input data samples and avoids the numerical problem the autocorrelation method suffers from. The lost frame is compensated from both forward and backward directions in which linear gain and weighting are applied. When BC speech is used as the input speech data for PLC in the case where the speech sender is in noisy environments, BC speech behaves more accurately than the corresponding AC speech, resulting in an excellent performance of the LP‐based PLC technique. This unveils a useful use of BC speech in speech information systems. Experiments show that in severe noise environments of AC speech‐to‐noise ratio being less than 10 dB, BC speech is superior to AC speech for PLC. It is also shown that the transmitted BC speech is more accurately reconstructed for PLC than transmitted AC speech is done. © 2023 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Pervasive computing with voice application service must be providing unprecedented levels of technology in order to adapt the continually changed computing resources in ubiquitous environment. A number of conditions differ from the speech signal over IP network with heterogeneous devices while voice traffic is transported by packet-based in ubiquitous environment. In this paper, voice signals in packet loss and noise is controlled for the guaranteed quality of service (QoS) on the IP network which are responsible for digitizing, encoding, decoding, and playing out the speech signal. We implement the algorithms of packet loss concealment (PLC) and noise reduction, which is called PEA (pervasive environment adaptation)-PLC, PEA-noise reduction together, for providing high quality voice-based pervasive computing services. The proposed PEA-PLC algorithms are applied as post-processing after decoding on devices, and then PEA-noise reduction method is applied as pre-processing before speech coding. The proposed PEA-algorithms for packet loss and noise are applied in G.729A codec optimally using mobile-softphone and a 1-port gateway PSTN phone system. The performances of the proposed methods are evaluated by the PESQ measurement in various packet loss and noisy conditions

Since the G.722 wideband speech codec offers higher quality and naturalness than G.711, is low in complexity, has low delay, and tandems well with other codecs, it is an attractive codec for voice over IP and voice over wireless LANs. However, packet losses in G.722 not only require good concealment of the lost frame, but a lost frame results in a mismatch of the encoder/decoder states for the next correctly received frame following the lost frame. Although proprietary schemes exist, the G.722 codec has no standardized packet loss concealment (PLC) method. We present a new PLC method for G.722 and propose an efficient approach to sending the side information to resynchronize the encoder and decoder that greatly improves the robustness of the G.722 codec to packet losses

Phase unwrapping based packet loss concealment using deep neural networks

The video quality degradation in wireless network is much changeable for complex packet loss scenes of wireless channels. However, few Video Quality Metrics (VQM) analyze the quality degradation under different wireless packet loss conditions. This paper proposes a novel Universal Video Quality Metric (UVQM), which is suitable for both distributed loss situation and burst loss situation. The major contributions of this paper can be concluded as follows. Firstly, this metric is an end-to-end measurement which considers the impacts of encoding parameter (quantization parameter, q-scale) and transmission parameters (Packet Loss Rate, PLR and Maximum Transmission Unit, MTU) to make a complete VQM. Then, parameters used in UVQM are extracted from bitstreams which reduces the use of computation resource for decoding at the receiver. Finally, UVQM coalesces the impact of content-aware features by introducing a developed Just Noticeable Distortion (JND) which makes the prediction much accuracy. The verification results show that the predicted video quality measured by Mean Opinion Score (MOS) has a high Pearson correlation (higher than 0.74 in distributed loss situation and 0.65 in burst loss situation).

Packet loss concealment algorithm using speech recognition and synthesis

Real-time multimedia communication due to its stringent delay requirements needs low delay PLC (packet loss concealment) methods to reduce the effect of lost packets. For audiovisual communications several PLC methods are utilized such as zero substitution or waveform substitution, we compare zero substitution and waveform repetition for a Gaussian source on the Gilbert-Elliott channel model. This allows to model the decrease of distortion with increasing buffer length

This paper has evaluated the system performance for the voice over Internet protocol (VoIP) services supported by the IEEE 802.16 systems in terms of the packet loss and delay, and it has also calculated the mean opinion score (MOS) in order to estimate the user's satisfaction for the VoIP services. By the simulation results, the MOS of the VoIP service supported by the unsolicited grant service (UGS) without auto repeat request (ARQ) has been decreased by 1.5 when the channel quality is poor. The reason of the MOS reduction is the increase of the packet loss due to the channel error. For this reason, this paper has considered the ARQ in order to compensate the packet loss when the channel quality is poor. The simulation results have shown that the extended real-time polling service (ertPS) and rtPS with ARQ can compensate the packet loss even if the channel quality is poor, however they can cause the reduction of the VoIP capacity because of the resource used for the retransmission. From the simulation results, the ertPS could save the resource by about 25 % compared to the rtPS with guaranteeing a quality of service (QoS).

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).