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Speech Signal Processing Based on Deep Learning in Complex Acoustic Environments
- 1st Edition - September 4, 2024
- Author: Xiao-Lei Zhang
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 4 8 5 6 - 6
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 4 8 5 7 - 3
Speech Signal Processing Based on Deep Learning in Complex Acoustic Environments provides a detailed discussion of deep learning-based robust speech processing and its applic… Read more
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Request a sales quoteSpeech 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
Senior undergraduate students, graduate students, and professionals with a solid foundation in speech signal processing and machine learning who are engaged in intelligent speech processing
- Cover image
- Title page
- Table of Contents
- Copyright
- Chapter 1: Introduction of the book
- Abstract
- Chapter 2: Fundamentals of deep learning
- Abstract
- 2.1. Supervised learning
- 2.2. Single-layer neural network
- 2.3. Feedforward deep neural network
- 2.4. Recurrent neural networks (RNN)
- 2.5. Convolutional neural networks
- 2.6. Normalization in neural networks
- 2.7. Attention mechanism in neural networks
- 2.8. Generative adversarial networks
- 2.9. Summary of this chapter
- References
- Chapter 3: Voice activity detection
- Abstract
- 3.1. Introduction
- 3.2. Fundamental knowledge
- 3.3. Voice activity detection models
- 3.4. Loss function of voice activity detection models
- 3.5. Acoustic features for voice activity detection
- 3.6. Generalization ability of models
- 3.7. Summary of this chapter
- References
- Chapter 4: Single-channel speech enhancement
- Abstract
- 4.1. Introduction
- 4.2. Fundamental knowledge
- 4.3. Frequency domain speech enhancement
- 4.4. Time domain speech enhancement
- 4.5. Summary of this chapter
- References
- Chapter 5: Multichannel speech enhancement
- Abstract
- 5.1. Introduction
- 5.2. Signal models
- 5.3. Spatial feature extraction
- 5.4. Beamforming methods
- 5.5. Ad-hoc microphone array method
- 5.6. Summary of this chapter
- References
- Chapter 6: Multispeaker speech separation
- Abstract
- 6.1. Introduction
- 6.2. Signal models
- 6.3. Speaker-dependent speech separation methods
- 6.4. Speaker-independent speech separation
- 6.5. Summary of this chapter
- References
- Chapter 7: Speaker recognition
- Abstract
- 7.1. Introduction
- 7.2. Speaker verification
- 7.3. Speaker diarization
- 7.4. Robust speaker verification
- 7.5. Summary of this chapter
- References
- Chapter 8: Speech recognition
- Abstract
- 8.1. Introduction
- 8.2. Speech recognition fundamentals
- 8.3. Evaluation metrics
- 8.4. End-to-end speech recognition
- 8.5. Noise-robust methods for speech recognition
- 8.6. Speaker adaptation
- 8.7. Summary of this chapter
- References
- References
- References
- Index
- No. of pages: 400
- Language: English
- Edition: 1
- Published: September 4, 2024
- Imprint: Elsevier
- Paperback ISBN: 9780443248566
- eBook ISBN: 9780443248573
XZ
Xiao-Lei Zhang
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.
Affiliations and expertise
Northwestern Polytechnical University, Xi'an, ChinaRead Speech Signal Processing Based on Deep Learning in Complex Acoustic Environments on ScienceDirect