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A Multi-Scale Feature Recalibration Network for End-to-End Single Channel Speech Enhancement

Lookup NU author(s): Yang Xian, Dr Yang Sun, Dr Mohsen Naqvi



This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2021.

For re-use rights please refer to the publisher's terms and conditions.


Deep neural networks based methods dominate recent development in single channel speech enhancement. In this paper, we propose a multi-scale feature recalibration convo-lutional encoder-decoder with bidirectional gated recurrent unit (BGRU) architecture for end-to-end speech enhancement. More specifically, multi-scale recalibration 2-D convolutional layers are used to extract local and contextual features from the signal. In addition, a gating mechanism is used in the recalibration network to control the information flow among the layers, which enables the scaled features to be weighted in order to retain speech and suppress noise. The fully connected layer (FC) is then employed to compress the output of the multi-scale 2-D convolutional layer with a small number of neurons, thus capturing the global information and improving parameter efficiency. The BGRU layers employ forward and backward GRUs, which contain the reset, update, and output gates, to exploit the interdependency among the past, current and future frames to improve predictions. The experimental results confirm that the proposed MCGN method outperforms several state-of-the-art methods.

Publication metadata

Author(s): Xian Y, Sun Y, Wang W, Naqvi SM

Publication type: Article

Publication status: Published

Journal: IEEE Journal of Selected Topics in Signal Processing

Year: 2021

Volume: 15

Issue: 1

Pages: 143-155

Print publication date: 01/01/2021

Online publication date: 18/12/2020

Acceptance date: 11/12/2020

Date deposited: 15/12/2020

ISSN (print): 1932-4553

ISSN (electronic): 1941-0484

Publisher: IEEE


DOI: 10.1109/JSTSP.2020.3045846


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