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
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.