ConSep: a Noise- and Reverberation-Robust Speech Separation Framework by Magnitude Conditioning
<|reference_start|>ConSep: a Noise- and Reverberation-Robust Speech Separation Framework by Magnitude Conditioning: 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.<|reference_end|>
@article{ho2024consep:,
    title={ConSep: a Noise- and Reverberation-Robust Speech Separation Framework by
  Magnitude Conditioning},
    author={Kuan-Hsun Ho, Jeih-weih Hung, and Berlin Chen},
    journal={arXiv preprint arXiv:2403.01792},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.01792},
    primaryClass={cs.SD eess.AS}
}