In this paper, we propose the regression-based packet loss concealment (PLC) for digital speech transmission by using deep neural networks (DNNs) with a multiple-layer deep architecture. For the DNN training, log-power spectra and phases are employed as features in the input layer for the large training set, which ensures non-linear mapping the frames from the last correctly received frame to the missing frame. Once the training is accomplished by the restricted Boltzmann machine (RBM)-based pre-training to initialize the DNN, minimum mean square error (MMSE)-based fine tuning is then performed based on the back-propagation algorithm. In the reconstruction stage, the trained DNN model is fed with the features of the previous frames in order to estimate the log-power spectra and phases of the missing frames. Reconstruction is further improved by using the cross-fading technique to mitigate discontinuity between the reconstruction signal and good frame signal in the time-domain. To demonstrate the performance of the proposed algorithm, hidden Markov model (HMM)-based PLC algorithm and the PLC algorithm standardized in adaptive multi-rate wideband (AMR-WB) Appendix I were used for comparison. The experimental results show that the proposed approach provides better speech quality and speech recognition accuracy than the conventional approaches.

In this paper, we propose an improved adaptive muting method using a sigmoid function for the packet loss concealment algorithm of ITU-T G.722 Recommendation. The packet loss concealment algorithm performs an adaptive muting to prevent the generation of unnecessary noise during packet loss recovery. While muting is linearly and discontinuously performed according to packet errors, our muting approach is performed by the non-linear and continuous sigmoid function. The principal parameters of the sigmoid function are obtained based on training at which minimization between the desired signal and the reconstructed signal is performed. Experimental results show that this proposed muting technique can enhance the performance of the packet loss concealment algorithm of G.722 under various packet loss environments.