# DNN driven Speaker Independent Audio-Visual Mask Estimation for Speech Separation

arXiv: https://arxiv.org/abs/1808.00060 · HTML: https://mlsanity.com/paper/1808.00060/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2018.

## Built on by (most cited first, 4 of 4)

- [Context-sensitive neocortical neurons transform the effectiveness and efficiency of neural information processing](https://mlsanity.com/paper/2207.07338/) (2022)
- [Towards Robust Real-time Audio-Visual Speech Enhancement](https://mlsanity.com/paper/2112.09060/) (2021)
- [Towards Intelligibility-Oriented Audio-Visual Speech Enhancement](https://mlsanity.com/paper/2111.09642/) (2021)
- [Multi-Modal Multi-Correlation Learning for Audio-Visual Speech Separation](https://mlsanity.com/paper/2207.01197/) (2022)

## Abstract (arXiv)

Human auditory cortex excels at selectively suppressing background noise to focus on a target speaker. The process of selective attention in the brain is known to contextually exploit the available audio and visual cues to better focus on target speaker while filtering out other noises. In this study, we propose a novel deep neural network (DNN) based audiovisual (AV) mask estimation model. The proposed AV mask estimation model contextually integrates the temporal dynamics of both audio and noise-immune visual features for improved mask estimation and speech separation. For optimal AV features extraction and ideal binary mask (IBM) estimation, a hybrid DNN architecture is exploited to leverages the complementary strengths of a stacked long short term memory (LSTM) and convolution LSTM network. The comparative simulation results in terms of speech quality and intelligibility demonstrate significant performance improvement of our proposed AV mask estimation model as compared to audio-only and visual-only mask estimation approaches for both speaker dependent and independent scenarios.