Fully Quantized Neural Networks for Audio Source Separation

Fully Quantized Neural Networks for Audio Source Separation

Deep neural networks have shown state-of-the-art results in audio source separation tasks in recent years. However, deploying such networks, especially on edge devices, is challenging due to memory and computation requirements. In this work, we focus on quantization, a leading approach for addressin...

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Bibliographic Details
Main Authors: Elad Cohen, Hai Victor Habi, Reuven Peretz, Arnon Netzer
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Open Journal of Signal Processing
Subjects:
Source separation
quantization
DNN
SDR
compression
knowledge distillation
Online Access:https://ieeexplore.ieee.org/document/10591369/

Internet

https://ieeexplore.ieee.org/document/10591369/

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