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
previous SOTA method using only 10% of the parameters and 18% of the MACs. This
is the first time-frequency domain audio-visual speech separation method to
outperform all contemporary time-domain counterparts.

本文提出了一种新颖的基于时频域的音视频语音分离方法：递归时频分离网络 (RTFS-Net)，通过在短时傅里叶变换产生的复杂时频区间上运用算法来独立地对音频的时间和频率进行建模，并引入了独特的基于注意力的融合技术，以有效地整合音频和视觉信息，并利用声学特征的固有谱特性进行更清晰的分离。RTFS-Net 在仅使用 10% 的参数和 18% 的 MAC 时，超越了先前的最先进方法。这是首个在时频域中超越所有当代时域对应方法的音视频语音分离方法。