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Overlapping speech separation gets a boost from SR-CorrNet, which disentangles speakers earlier using a novel separation-reconstruction strategy and correlation-based filtering, leading to state-of-the-art results in noisy and reverberant 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.