Phase aware speech enhancement based on depthwise deep convolutional network

Deep neural network-based Speech Enhancement (SE) techniques aim to improve the clarity and quality of speech signals where neural models are trained to recover clean speech from noisy mixtures. Most of these techniques focus on estimating the magnitude of the spectrogram while reusing the phase com...

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Bibliographic Details
Published in:	Signal, image and video processing Vol. 19; no. 9; p. 757
Main Authors:	Iqbal, Yasir, Zhang, Tao, Iqbal, Anjum, Azaz, Ikram, Sadique, Umar, Fahad, Muhammad, Zhao, Xin, Geng, Yanzhang
Format:	Journal Article
Language:	English
Published:	London Springer London 01.09.2025 Springer Nature B.V
Subjects:	Artificial neural networks Computer Imaging Computer Science Datasets Deep learning Distortion Estimation Fourier transforms Image Processing and Computer Vision Intelligibility Machine learning Mapping Multimedia Information Systems Neural networks Noise Original Paper Parallel processing Pattern Recognition and Graphics Signal quality Signal,Image and Speech Processing Software radio Spectrograms Speech Speech processing Vision Speech enhancement Depthwise separable Convolution Signal-to-Distortion ratio (SDR) Complex-Valued spectrograms Depthwise deep convolutional network
ISSN:	1863-1703, 1863-1711
Online Access:	Get full text
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