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This paper introduces a knowledge distillation (KD) approach to enhance the efficiency of acoustic echo control (AEC) models, addressing the high computational complexity of machine-learned methods compared to classical algorithms. By employing a CGGN16 student model, the authors achieve a significant reduction in computational cost to just 2% of the teacher model while maintaining superior performance and reduced near-end speech distortion. This work marks a significant advancement in AEC, demonstrating that effective KD can bridge the gap between model complexity and performance without compromising quality.
Achieving 98% computational savings while improving AEC performance reveals the untapped potential of knowledge distillation in audio processing.
In recent years, many efforts have been made to supersede classical acoustic echo control (AEC) algorithms with more powerful machine-learned approaches. While surpassing the performance of well-established adaptive filters is very much possible, a remaining challenge is computational complexity. Popular architectures, such as convolutional recurrent networks (CRNs), are by multiple orders of magnitude computationally more expensive than classical signal processing solutions. Scaling down such models is usually straight-forward, but it comes at the cost of a notably reduced performance. We show - to the author's knowledge for the first time in AEC - how these performance drops can be successfully alleviated to a large degree by employing an effective knowledge distillation (KD) process, enabling more potent efficient AEC. Our 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.
