USDnet: Unsupervised Speech Dereverberation via Neural Forward Filtering
arxiv(2024)
摘要
In reverberant conditions with a single speaker, each far-field microphone
records a reverberant version of the same speaker signal at a different
location. In over-determined conditions, where there are more microphones than
speakers, each recorded mixture signal can be leveraged as a constraint to
narrow down the solutions to target anechoic speech and thereby reduce
reverberation. Equipped with this insight, we propose USDnet, a novel deep
neural network (DNN) approach for unsupervised speech dereverberation (USD). At
each training step, we first feed an input mixture to USDnet to produce an
estimate for target speech, and then linearly filter the DNN estimate to
approximate the multi-microphone mixture so that the constraint can be
satisfied at each microphone, thereby regularizing the DNN estimate to
approximate target anechoic speech. The linear filter can be estimated based on
the mixture and DNN estimate via neural forward filtering algorithms such as
forward convolutive prediction. We show that this novel methodology can promote
unsupervised dereverberation of single-source reverberant speech.
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