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By disentangling speakers earlier in the process, SR-CorrNet avoids the information bottleneck that plagues existing speech separation models, leading to improved performance in challenging acoustic environments.
By shifting the learning objective from direct spectral mapping to filter estimation based on inter-frame correlations, IF-CorrNet achieves state-of-the-art monaural speech dereverberation performance, particularly in real-world environments where generalization is critical.
