  Audio-visual speech separation methods aim to integrate different modalities
to generate high-quality separated speech, thereby enhancing the performance of
downstream tasks such as speech recognition. Most existing state-of-the-art
(SOTA) models operate in the time domain. However, their overly simplistic
approach to modeling acoustic features often necessitates larger and more
computationally intensive models in order to achieve SOTA performance. In this
paper, we present a novel time-frequency domain audio-visual speech separation
method: Recurrent Time-Frequency Separation Network (RTFS-Net), which applies
its algorithms on the complex time-frequency bins yielded by the Short-Time
Fourier Transform. We model and capture the time and frequency dimensions of
the audio independently using a multi-layered RNN along each dimension.
Furthermore, we introduce a unique attention-based fusion technique for the
efficient integration of audio and visual information, and a new mask
separation approach that takes advantage of the intrinsic spectral nature of
the acoustic features for a clearer separation. RTFS-Net outperforms the prior
SOTA method in both inference speed and separation quality while reducing the
number of parameters by 90% and MACs by 83%. This is the first time-frequency
domain audio-visual speech separation method to outperform all contemporary
time-domain counterparts.
