  Existing deep learning based speech enhancement (SE) methods either use blind
end-to-end training or explicitly incorporate speaker embedding or phonetic
information into the SE network to enhance speech quality. In this paper, we
perceive speech and noises as different types of sound events and propose an
event-based query method for SE. Specifically, representative speech embeddings
that can discriminate speech with noises are first pre-trained with the sound
event detection (SED) task. The embeddings are then clustered into fixed golden
speech queries to assist the SE network to enhance the speech from noisy audio.
The golden speech queries can be obtained offline and generalizable to
different SE datasets and networks. Therefore, little extra complexity is
introduced and no enrollment is needed for each speaker. Experimental results
show that the proposed method yields significant gains compared with baselines
and the golden queries are well generalized to different datasets.
