Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

© 2007-2012 IEEE. Deep neural networks based methods dominate recent development in single channel speech enhancement. In this paper, we propose a multi-scale feature recalibration convolutional 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.

Original publication




Journal article


IEEE Journal on Selected Topics in Signal Processing

Publication Date





143 - 155