Preprint
Aug 2026
Knowledge Distillation for Efficient Acoustic Echo Control
These proposed CGGN16 student AEC models show significantly less near-end speech distortion at only 2% of its teacher's computational complexity, surpass the overall performance of a six times more complex model trained on ground-truth labels, and outperform other AEC-focused architectures from recent literature.
Ernst Seidel, Pejman Mowlaee, Tim Fingscheidt
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