Packet Loss Concealment Based on Phase Correction and Deep Neural Network

Abstract

In a packet switching network, the performance of packet loss concealment (PLC) is often affected by inaccurate estimation of phase spectrum of speech signal in the lost packet. In order to solve this problem, two kinds of PLC methods in the scene of continuous packet loss are proposed in this paper based on phase correction. One of them is based on waveform similarity overlap-add (WSOLA) and deep neural network (DNN), and the other one is based on the Griffin–Lim algorithm (GLA) and DNN. In the first method, considering the correlation of adjacent frames of speech signal, the periodicity of speech signal is well retained by the WSOLA method so that the phase spectrum of the lost signal is recovered. Combined with the prediction of amplitude spectrum of the lost signal by the DNN, the performance of the PLC in the case of continuous packet loss is effectively improved. In the second method, the phase spectrum of the lost signal is modified iteratively by the amplitude spectra estimated via the DNN based on the consistency of Fourier transform so that the phase spectra match the amplitude spectra. The PLC is greatly achieved as well. The experimental results show that the proposed PLC methods provide better speech quality than the reference methods.

Keywords

• packet loss concealment (PLC)
• deep neural network (DNN)
• waveform similarity overlap-add (WSOLA)
• Griffin–Lim algorithm
• phase correction