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Enhancement of Coded Speech Using a Mask-Based Post-Filter

: Korse, S.; Gupta, K.; Fuchs, G.


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society; IEEE Signal Processing Society:
IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2020. Proceedings : May 4-8, 2020, Barcelona, Spain
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-5090-6631-5
ISBN: 978-1-5090-6632-2
International Conference on Acoustics, Speech and Signal Processing (ICASSP) <45, 2020, Barcelona>
Fraunhofer IIS ()

The quality of speech codecs deteriorates at low bitrates due to high quantization noise. A post-filter is generally employed to enhance the quality of the coded speech. In this paper, a data-driven post-filter relying on masking in the time-frequency domain is proposed. A fully connected neural network (FCNN), a convolutional encoder-decoder (CED) network and a long short-term memory (LSTM) network are implemeted to estimate a real-valued mask per time-frequency bin. The proposed models were tested on the five lowest operating modes (6.65 kbps-15.85 kbps) of the Adaptive Multi-Rate Wideband codec (AMR-WB). Both objective and subjective evaluations confirm the enhancement of the coded speech and also show the superiority of the mask-based neural network system over a conventional heuristic post-filter used in the standard like ITU-T G.718.