Dynamic nsNET2: Efficient Deep Noise Suppression with Early Exiting

Dynamic nsNET2: Efficient Deep Noise Suppression with Early Exiting

Although deep learning has made strides in the field of deep noise suppression, leveraging deep architectures on resourceconstrained 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...

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Bibliographic Details
Published in:2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP) pp. 1 - 6
Main Authors: Miccini, Riccardo, Zniber, Alaa, Laroche, Clement, Piechowiak, Tobias, Schoeberl, Martin, Pezzarossa, Luca, Karrakchou, Ouassim, Sparso, Jens, Ghogho, Mounir
Format: Conference Proceeding
Language:English
Published: IEEE 17-09-2023
Subjects:
Computational modeling
Deep Noise Suppression
Dynamic Neural Networks
Early-exiting
Gray-scale
Headphones
Hearing aids
Noise reduction
Performance evaluation
Signal processing
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Summary:Although deep learning has made strides in the field of deep noise suppression, leveraging deep architectures on resourceconstrained 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.
ISSN:2161-0371
DOI:10.1109/MLSP55844.2023.10285925