Concetti Chiave
음성 분리 기술의 혁신적인 발전과 ConSep 프레임워크의 효과적인 성능 향상
Statistiche
Speech separation has made significant progress thanks to fine-grained vision in time-domain methods.
ConSep promotes performance in anechoic, noisy, and reverberant settings compared to SepFormer and Bi-Sep.
Time-domain methods usually perform better in SI-SDR and worse in PESQ than STFT methods.
A large enough window size is mandatory to avoid contravening the prerequisite of Multiplicative Transfer Function Approximation (MTFA).
Employing STFT representation exhibits optimal performance in reverberation.
Citazioni
"ConSep surpasses SepFormer under an anechoic condition and upgrades SepFormer under more complicated situations."
"Efforts to make SepFormer a more distilled yet versatile model need further investigation."
"ConSep outperforms all other methods except the SDRi, which can be deceived by the loudness."