Audio Signal Processing. Advanced Analysis and Machine Learning Applications

Audio is a fundamental modality of data that has immense utility and need in human communication, digital transmission, pattern discovery, ecological understanding, healthcare, and many more. Advancements in sensor technology, signal processing, and machine learning have led to audio as an asset in many vertical applications. Audio Signal Processing: Advanced Analysis and Machine Learning Applications covers the basics of audio signal processing and analysis from sensing, acquisition, digitization, filtering, denoising, modelling, feature analysis, machine learning, and pattern recognition/decision making. It provides practical examples, illustrations, lab exercises, and mini projects to enhance the learning experience.

1. Audio Signal Basics
2. Digitization of Audio Signals
3. Pre-processing of Audio Signals
4. Adaptive Analysis of Audio Signals
5. Advanced Analysis of Audio Signals
6. Machine Learning of Audio Signals
7. Auditory Display
8. Case Studies

Authors

Sri Krishnan Toronto Metropolitan University, Canada. Sri Krishnan joined Toronto Metropolitan University, Toronto, Canada in 1999, and he is now a Professor of Electrical, Computer Engineering, and Biomedical Engineering. Sri Krishnan's research interests include biomedical signal analysis, audio signal analysis, and explainable machine learning. He is a Fellow of the Canadian Academy of Engineering. From 2007 to 2017 he was a Canada Research Chair in Biomedical Signal Analysis. He has published more than 400 refereed papers in journals and conferences. Sri Krishnan is a recipient of the Outstanding Canadian Biomedical Engineer Award, Achievement in Innovation Award from Innovate Calgary, Sarwan Sahota Distinguished Scholar Award, Young Engineer Achievement Award from Engineers Canada, New Pioneers Award in Science and Technology, and Exemplary Service Award from the IEEE Toronto Section.

Table of Contents

Authors

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