# Enhancement Of Coded Speech Using a Mask-Based Post-Filter

arXiv: https://arxiv.org/abs/2010.05571 · HTML: https://mlsanity.com/paper/2010.05571/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [NESC: Robust Neural End-2-End Speech Coding with GANs](https://mlsanity.com/paper/2207.03282/) (2022)
- [A DNN Based Post-Filter to Enhance the Quality of Coded Speech in MDCT Domain](https://mlsanity.com/paper/2201.12039/) (2022)

## Abstract (arXiv)

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.