Work

Dynamic nsNET2: Efficient Deep Noise Suppression with Early Exiting

Year: 2023

Type: conference-paper

Abstract: 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.

Source: IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

DOI: 10.1109/mlsp55844.2023.10285925

Authors: Riccardo Miccini, Alaa Zniber, Clément Laroche, Tobias Piechowiak, Martin Schoeberl, Luca Pezzarossa, Ouassim Karrakchou, Jens Sparsø, Mounir Ghogho

Institutions: Technical University of Denmark, International University of Rabat

Language: English

Topic: Speech and Audio Processing

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4387870004

FWCI: 1.471

Cites: 19

Cited by: 7

Related to: 10