Signal Processing and Machine Learning with Applications

Signal processing captures, interprets, describes and manipulates physical phenomena. Mathematics, statistics, probability, and stochastic processes are among the signal processing languages we use to interpret real-world phenomena, model them, and extract useful information. This book presents the different kinds of signals that humans and machines use to communicate, and their treatments and applications. The main topic is stochastic processes of signals that are useful for applications. 

In Part A the authors present the fundamentals of signal processing, signal transformation, and spectral analysis. The chapters in Part B cover machine learning and recognition issues such as general learning, stochastic processes, feature extraction, probability theory, unsupervised learning, Markov models, fuzzy logic and rough sets, and neural networks. Part C addresses practical implementation aspects, in particular noise and audio and speech recognition, and then the authors give insights on how to apply the techniques explained in domains such as biomedicine, seismology, visual analytics, visual storytelling, emergency medicine, and interactive communications. 

The book is intended for advanced undergraduate and postgraduate students, researchers and practitioners who are engaged with signal processing and its applications.

Prof. Michael M. Richter completed his PhD on mathematical logic at the University of Freiburg, and his Habilitation in mathematics at the University of Tübingen. He taught at the University of Texas at Austin and at RWTH Aachen, in addition to numerous visiting professorships. Most recently he held a chair in computer science at the University of Kaiserslautern, where he was also the founding scientific director of the DFKI (German Research Center for Artificial Intelligence). He was also an adjunct professor at the University of Calgary. He taught, researched, and published extensively in the areas of mathematical logic and artificial intelligence. Prof. Richter was one of the pioneers of case-based reasoning: he founded the leading European event on the subject, he led many of the key academic research projects, and he demonstrated the real-world viability of the approach with successful commercial products. Prof. Richter passed away during the final publishing phase of the project.

Dr. Sheuli Paul earned her PhD in electrical and computer engineering in the area of signal processing and machine learning from Kaiserslautern, Germany. She has been engaged in applied research and development in signal and image processing, artificial intelligence especially in the area of speech recognition, machine learning, visual data mining and computer vision. She is a defence scientist in autonomous robotics and human-robot interaction in the autonomous section of Defence Research and Development Canada, a Canadian government agency.