Audio-Visual Speech Separation in Noisy Environments with a Lightweight Iterative Model

• URL: http://arxiv.org/abs/2306.00160v1
• Date: Wed, 31 May 2023 20:09:50 GMT
• Title: Audio-Visual Speech Separation in Noisy Environments with a Lightweight Iterative Model
• Authors: H\'ector Martel, Julius Richter, Kai Li, Xiaolin Hu, Timo Gerkmann
• Abstract summary: We propose Audio-Visual Lightweight ITerative model (AVLIT) to perform audio-visual speech separation in noisy environments. Our architecture consists of an audio branch and a video branch, with iterative A-FRCNN blocks sharing weights for each modality. Experiments demonstrate the superiority of our model in both settings with respect to various audio-only and audio-visual baselines.
• Score: 35.171785986428425
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose Audio-Visual Lightweight ITerative model (AVLIT), an effective and lightweight neural network that uses Progressive Learning (PL) to perform audio-visual speech separation in noisy environments. To this end, we adopt the Asynchronous Fully Recurrent Convolutional Neural Network (A-FRCNN), which has shown successful results in audio-only speech separation. Our architecture consists of an audio branch and a video branch, with iterative A-FRCNN blocks sharing weights for each modality. We evaluated our model in a controlled environment using the NTCD-TIMIT dataset and in-the-wild using a synthetic dataset that combines LRS3 and WHAM!. The experiments demonstrate the superiority of our model in both settings with respect to various audio-only and audio-visual baselines. Furthermore, the reduced footprint of our model makes it suitable for low resource applications.

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