  Audio-visual target speaker extraction (AV-TSE) aims to extract the specific
person's speech from the audio mixture given auxiliary visual cues. Previous
methods usually search for the target voice through speech-lip synchronization.
However, this strategy mainly focuses on the existence of target speech, while
ignoring the variations of the noise characteristics. That may result in
extracting noisy signals from the incorrect sound source in challenging
acoustic situations. To this end, we propose a novel reverse selective auditory
attention mechanism, which can suppress interference speakers and non-speech
signals to avoid incorrect speaker extraction. By estimating and utilizing the
undesired noisy signal through this mechanism, we design an AV-TSE framework
named Subtraction-and-ExtrAction network (SEANet) to suppress the noisy
signals. We conduct abundant experiments by re-implementing three popular
AV-TSE methods as the baselines and involving nine metrics for evaluation. The
experimental results show that our proposed SEANet achieves state-of-the-art
results and performs well for all five datasets. We will release the codes, the
models and data logs.
