SR-CorrNet introduces an asymmetric TF-domain architecture with separation-reconstruction strategy and correlation-to-filter estimation that yields consistent gains on WSJ0-Mix, WHAMR!, and LibriCSS under anechoic, noisy-reverberant, and real-recorded conditions.
MP-SENet: A speech enhancement model with parallel denoising of magnitude and phase spectra
2 Pith papers cite this work. Polarity classification is still indexing.
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A magnitude-phase dual-stream framework enforcing global rotation equivariance for improved phase modeling across speech enhancement tasks including denoising and dereverberation.
citing papers explorer
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Asymmetric Encoder-Decoder Based on Time-Frequency Correlation for Speech Separation
SR-CorrNet introduces an asymmetric TF-domain architecture with separation-reconstruction strategy and correlation-to-filter estimation that yields consistent gains on WSJ0-Mix, WHAMR!, and LibriCSS under anechoic, noisy-reverberant, and real-recorded conditions.
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Global Rotation Equivariant Phase Modeling for Speech Enhancement with Deep Magnitude-Phase Interaction
A magnitude-phase dual-stream framework enforcing global rotation equivariance for improved phase modeling across speech enhancement tasks including denoising and dereverberation.