  Real-time single-channel speech separation aims to unmix an audio stream
captured from a single microphone that contains multiple people talking at
once, environmental noise, and reverberation into multiple de-reverberated and
noise-free speech tracks, each track containing only one talker. While large
state-of-the-art DNNs can achieve excellent separation from anechoic mixtures
of speech, the main challenge is to create compact and causal models that can
separate reverberant mixtures at inference time. In this paper, we explore
low-complexity, resource-efficient, causal DNN architectures for real-time
separation of two or more simultaneous speakers. A cascade of three neural
network modules are trained to sequentially perform noise-suppression,
separation, and de-reverberation. For comparison, a larger end-to-end model is
trained to output two anechoic speech signals directly from noisy reverberant
speech mixtures. We propose an efficient single-decoder architecture with
subtractive separation for real-time recursive speech separation for two or
more speakers. Evaluation on real monophonic recordings of speech mixtures,
according to speech separation measures like SI-SDR, perceptual measures like
DNS-MOS, and a novel proposed channel separation metric, show that these
compact causal models can separate speech mixtures with low latency, and
perform on par with large offline state-of-the-art models like SepFormer.

Audio and Speech Processing;Sound