A comprehensive reference book for practical engineers and researchers focusing on neural network applications on signal processing, this text concerns itself with a vast array of signals. Among included signals, readers will find information on audio, video, speech, communication, geophysical, sonar, radar and medical signals. Written for those at the professional and academics levels, as well as for those in both graduate and undergraduate study, this book provides a detailed treatment of neural networks for signal processing.

The field of artificial neural networks has made tremendous progress in the past 20 years in terms
of theory, algorithms, and applications. Notably, the majority of real world neural network applications
have involved the solution of difficult statistical signal processing problems. Compared to
conventional signal processing algorithms that are mainly based on linear models, artificial neural
networks offer an attractive alternative by providing nonlinear parametric models with universal
approximation power, as well as adaptive training algorithms. The availability of such powerful
modeling tools motivated numerous research efforts to explore new signal processing applications
of artificial neural networks. During the course of the research, many neural network paradigms were
proposed. Some of them are merely reincarnations of existing algorithms formulated in a neural
network-like setting, while the others provide new perspectives toward solving nonlinear adaptive
signal processing. More importantly, there are a number of emergent neural network paradigms that
have found successful real world applications.