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arXiv · 2308.16678

Dynamic nsNet2: Efficient Deep Noise Suppression with Early Exiting

Riccardo Miccini·Alaa Zniber·Clément Laroche·Tobias Piechowiak·Martin Schoeberl·Luca Pezzarossa·Ouassim Karrakchou·Jens Sparsø·Mounir Ghogho
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Abstract

Although deep learning has made strides in the field of deep noise suppression, leveraging deep architectures on resource-constrained devices still proved challenging. Therefore, we present an early-exiting model based on nsNet2 that provides several levels of accuracy and resource savings by halting computations at different stages. Moreover, we adapt the original architecture by splitting the information flow to take into account the injected dynamism. We show the trade-offs between performance and computational complexity based on established metrics.

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Riccardo Miccini, Alaa Zniber, Clément Laroche, Tobias Piechowiak, Martin Schoeberl, Luca Pezzarossa, Ouassim Karrakchou, Jens Sparsø, Mounir Ghogho. 2023-08-31. Dynamic nsNet2: Efficient Deep Noise Suppression with Early Exiting. https://doi.org/10.1109/mlsp55844.2023.10285925

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