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 different kinds of signals humans use and applies them for human machine interaction to communicate. 

Signal Processing and Machine Learning with Applications presents methods that are used to perform various Machine Learning and Artificial Intelligence tasks in conjunction with their applications. It is organized in three parts: Realms of Signal Processing; Machine Learning and Recognition; and Advanced Applications and Artificial Intelligence. The comprehensive coverage is accompanied by numerous examples, questions with solutions, with historical notes. The book is intended for advanced undergraduate and postgraduate students, researchers and practitioners who are engagedwith signal processing, machine learning and the applications. 

• Signal Processing
• Machine Learning
• Stochastic Processes
• Markov Model
• Hidden Stochastic Model (HSM)
• Feature Extraction
• Noise

• Electrical and Computer Engineering, University of Kaiserslautern, Kaiserslautern, Germany

  Michael M. Richter

• FB Elektrotechnik & Informationstechnik, Technische Universität Kaiserslautern, Kaiserslautern, Germany

  Sheuli Paul

• Florida Institute of Technology, Melbourne, USA

  Veton Këpuska, Marius Silaghi

Professor Michael M. Richter taught at the University of Texas at Austin and at RWTH Aachen, in addition to numerous visiting professorships.  He is one of the founding scientific director of the DFKI (German Research Center for Artificial Intelligence). He taught, researched, and published extensively in the areas of mathematical logic and artificial intelligence. Professor 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 demonstrated the real-world viability of the approach with successful commercial products. Michael Richter passed away during the final publishing phase of this book.

Dr. Sheuli Paul is a scientist in Defence Research and Development Canada, engaged in  applied research in the areas of signal processing, machine learning, artificial intelligence and  human-robot interaction.   Trying to solve complex problems in interdisciplinary areas is her passion.

Dr. Veton  Këpuska  is an  inventor of  Wake-Up-Word Speech Recognition, a method of communication with machines for which he was granted two patents. He joined Florida Institute of Technology (FIT) in 2003 and engaged in numerous research activities in speech and image processing, digital processes, and machine learning. Dr. Këpuska won the First Annual Digital Signal Processing Design competition by applying his Wake-up-Word on embedded Analog Devices Platform. Dr. Këpuska won numerous awards including “the Kerry Bruce Clark” award for teaching excellence and received numerous best paper awards.

Prof. Marius Silaghi has taught, researched, and published in the areas of artificial intelligence and networking. Professor Silaghi is involved in human-machine interaction research and proposed techniques for motion capture, speech recognition, and robotics. He founded the conference on Distributed Constraint Optimization and gave multiple tutorials on the topic at the main artificial intelligence conferences. He received numerous best paper awards.

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