Speech Signal Processing Based on Deep Learning in Complex Acoustic Environments

Speech Signal Processing Based on Deep Learning in Complex Acoustic Environments provides a detailed discussion of deep learning-based robust speech processing and its applications. The book begins by looking at the basics of deep learning and common deep network models, followed by front-end algorithms for deep learning-based speech denoising, speech detection, single-channel speech enhancement multi-channel speech enhancement, multi-speaker speech separation, and the applications of deep learning-based speech denoising in speaker verification and speech recognition. - Provides a comprehensive introduction to the development of deep learning-based robust speech processing - Covers speech detection, speech enhancement, dereverberation, multi-speaker speech separation, robust speaker verification, and robust speech recognition - Focuses on a historical overview and then covers methods that demonstrate outstanding performance in practical applications

Xiao-Lei Zhang received his Ph.D. degree with honors from Tsinghua University, China, in 2012. He was a postdoctoral researcher with the Department of Electronic Engineering at Tsinghua University from 2012 to 2014. He was a visiting scholar at The Ohio State University, USA, from 2013 to 2014 and a postdoctoral researcher with the Department of Computer Science and Engineering, The Ohio State University, from 2014 to 2016. Since 2016 he has been a full professor at the Northwestern Polytechnical University, Xi'an, China. His research interests are the topics in speech processing, machine learning, statistical signal processing, and artificial intelligence.