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Packet Loss Concealment Based on Deep Neural Networks for Digital Speech Transmission

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Abstract

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.This work was supported by the National Research Foundation of Korea (NRF) funded by the Korean government (MSIP) under Grant 2014R1A2A1A10049735

Topics: Adaptive multi-rate wideband, deep neural network (DNN), network speech recognition, packet loss concealment (PLC), regression model, speech quality
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Year: 2016
DOI identifier: 10.1109/TASLP.2015.2509780
OAI identifier: oai:repository.hanyang.ac.kr:20.500.11754/34115
Provided by: HANYANG Repository
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