ConSep: a Noise- and Reverberation-Robust Speech Separation Framework by Magnitude Conditioning
DSP(2024)
摘要
Speech separation has recently made significant progress thanks to the
fine-grained vision used in time-domain methods. However, several studies have
shown that adopting Short-Time Fourier Transform (STFT) for feature extraction
could be beneficial when encountering harsher conditions, such as noise or
reverberation. Therefore, we propose a magnitude-conditioned time-domain
framework, ConSep, to inherit the beneficial characteristics. The experiment
shows that ConSep promotes performance in anechoic, noisy, and reverberant
settings compared to two celebrated methods, SepFormer and Bi-Sep. Furthermore,
we visualize the components of ConSep to strengthen the advantages and cohere
with the actualities we have found in preliminary studies.
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