Bilateral Waveform Similarity Overlap-and-Add Based Packet Loss Concealment for Voice over IP

This paper invested a bilateral waveform similarity overlap-and-add algorithm for voice packet lost. Since Packet lost will cause the semantic misunderstanding, it has become one of the most essential problems in speech communication. This investment is based on waveform similarity measure using overlap-and-Add algorithm and provides the bilateral information to enhance the speech signal reconstruction. Traditionally, it has been improved that waveform similarity overlap-and-add (WSOLA) technique is an effective algorithm to deal with packet loss concealment (PLC) for real-time time communication. WSOLA algorithm is widely applied to deal with the length adaptation and packet loss concealment of speech signal. Time scale modification of audio signal is one of the most essential research topics in data communication, especially in voice of IP (VoIP). Herein, the proposed the bilateral WSOLA (BWSOLA) that is derived from WSOLA. Instead of only exploitation one direction speech data, the proposed method will reconstruct the lost voice data according to the preceding and cascading data. The related algorithms have been developed to achieve the optimal reconstructing estimation. The experimental results show that the quality of the reconstructed speech signal of the bilateral WSOLA is much better compared to the standard WSOLA and GWSOLA on different packet loss rate and length using the metrics PESQ and MOS. The significant improvement is obtained by bilateral information and proposed method. The proposed bilateral waveform similarity overlap-and-add (BWSOLA) outperforms the traditional approaches especially in the long duration data loss

Adaptive recovery techniques for real-time audio streams

There are a number of packet-loss recovery techniques proposed for streaming audio applications recently. However, there are few works that are able to exploit the tradeoff between the recovery quality and the computational complexity. In this paper, we develop a recovery method, called DSPWR (Double Sided PitchWaveform Replication) which is able to tolerate a much higher packet loss rate. In essence, DSPWR is composed of several procedures devised to improve the quality of the reconstructed speech. It is noted that amore sophisticated recovery scheme that can tolerate a higher degree of packet loss in general requires a larger computational cost. In view of this, we evaluate the quality of the reconstructed speech under different packet loss rates for various receiver-based recovery methods, and compare the computational complexity among these methods. Under the acceptable speech quality whose MOS (Mean Opinion Score) is above 3.5, we develop an adaptive mechanism that can select the recovery method with the minimal complexity in accordance with different packet loss rates encountered. To conduct real experiments in the networks, we implement these recovery methods and evaluate the performance of DSPWR devised and the adaptive recovery techniques empirically. As validated by our experimental results, the adaptive mechanism is able to strike a compromise between the computational overhead and the quality of the speech desired

Adaptive Recovery Techniques for Real-Time Audio Streams

There are a number of packet-loss recovery techniques proposed for streaming audio applications recently. However, there are few works that are able to exploit the tradeoff between the recovery quality and the computational complexity. In this paper, we develop a recovery method, called DSPWR (Double Sided PitchWaveform Replication) which is able to tolerate a much higher packet loss rate. In essence, DSPWR is composed of several procedures devised to improve the quality of the reconstructed speech. It is noted that amore sophisticated recovery scheme that can tolerate a higher degree of packet loss in general requires a larger computational cost. In view of this, we evaluate the quality of the reconstructed speech under different packet loss rates for various receiver-based recovery methods, and compare the computational complexity among these methods. Under the acceptable speech quality whose MOS (Mean Opinion Score) is above 3.5, we develop an adaptive mechanism that can select the recovery method with the minimal complexity in accordance with different packet loss rates encountered. To conduct real experiments in the networks, we implement these recovery methods and evaluate the performance of DSPWR devised and the adaptive recovery techniques empirically. As validated by our experimental results, the adaptive mechanism is able to strike a compromise between the computational overhead and the quality of the speech desired