A simple, nonrigorous, synthetic view of wavelet theory is presented for both review and tutorial purposes. The discussion includes nonstationary signal analysis, scale versus frequency, wavelet analysis and synthesis, scalograms, wavelet frames and orthonormal bases, the discrete-time case, and applications of wavelets in signal processing. The main definitions and properties of wavelet transforms are covered, and connections among the various fields where results have been developed are shown. >

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Wavelets and signal processing

Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. This paper summarizes some of the most important developments in neural network classification research. Specifically, the issues of posterior probability estimation, the link between neural and conventional classifiers, learning and generalization tradeoff in classification, the feature variable selection, as well as the effect of misclassification costs are examined. Our purpose is to provide a synthesis of the published research in this area and stimulate further research interests and efforts in the identified topics.

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Neural networks for classification: a survey

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

An efficient and flexible dictionary structure is proposed for sparse and redundant signal representation. The proposed sparse dictionary is based on a sparsity model of the dictionary atoms over a base dictionary, and takes the form D = ? A, where ? is a fixed base dictionary and A is sparse. The sparse dictionary provides efficient forward and adjoint operators, has a compact representation, and can be effectively trained from given example data. In this, the sparse structure bridges the gap between implicit dictionaries, which have efficient implementations yet lack adaptability, and explicit dictionaries, which are fully adaptable but non-efficient and costly to deploy. In this paper, we discuss the advantages of sparse dictionaries, and present an efficient algorithm for training them. We demonstrate the advantages of the proposed structure for 3-D image denoising.

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Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximation

1. Spoken language input Ronald Cole, Victor Zue, Wayne Ward, Melvyn J. Hunt, Richard M. Stern, Renato De Mori, Fabio Brugnara, Salim Roukos, Sadaoki Furui and Patti Price 2. Written language input Joseph Mariani, Sargur N. Srihari, Rohini K. Srihari, Richard G. Casey, Abdel Belaid, Claudie Faure, Eric Lecolinet, Isabelle Guyo, Colin Warwick and Rejean Plamondon 3. Language analysis and understanding Annie Zaenen, Hans Uszkoreit, Fred Karlsson, Lauri Karttunen, Antonio Sanfilippo, Stephen F. Pulman, Fernando Pereira and Ted Briscoe 4. Language generation Hans Uszkoreit, Eduard Hovy, Gertjan van Noord, Gunter Neumann and John Bateman 5. Spoken output technologies Ronald Cole, Yoshinori Sagisaka, Christophe d'Alessandro, Jean-Sylvain Lienard, Richard Sproat, Kathleen R. McKeown and Johanna D. Moore 6. Discourse and dialogue Hans Uszkoreit, Barbara Grosz, Donia Scott, Hans Kamp, Phil Cohe and Egidio Giachin 7. Document processing Annie Zaenen, Per-Kristian Halvorsen, Donna Harman, Peter Schauble, Alan Smeaton, Paul Jacobs, Karen Sparck Jones, Robert Dale, Richard H. Wojcik and James E. Hoard 8. Multilinguality Annie Zaenen, Martin Kay, Christian Boitet, Christian Fluhr, Alexander Waibel, Yeshwant K. Muthusamy and A. Lawrence Spitz 9. Multimodality Joseph Mariani, James L. Flanagan, Gerard Ligozat, Wolfgang Wahlster, Yacine Bellik, Alan J. Goldschen, Christian Benoit, Dominic W. Massaro and Michael M. Cohen 10. Transmission and storage Victor Zue, Isabel Trancoso, Bishnu S. Atal, Nikil S. Jayant and Dirk Van Compernolle 11. Mathematical methods Ronald Cole, Hans Uszkoreit, Steve Levinson, John Makhoul, Aravind Joshi, Herve Bourlard, Nelson Morgan, Ronald M. Kaplan and John Bridle 12. Language resources Ronald Cole, Antonio Zampolli, Eva Ejerhed, Ken Church, Lori Lamel, Ralph Grishman, Nicoletta Calzolari, Christian Galinski and Gerhard Budin 13. Evaluation Joseph Mariani, Lynette Hirschman, Henry S. Thompson, Beth Sundheim, John Hutchins, Ezra Black, Margaret King, David S. Pallett, Adrian Fourcin, Louis C. W. Pols, Sharon Oviatt, Herman J. M. Steeneken and Junichi Kanai Glossary Citation index Index.

Survey of the State of the Art in Human Language Technology

Methods are presented for adaptively generating wavelet templates for signal representation and classification using neural networks. Different network structures and energy functions are necessary and are given for representation and classification. The idea is introduced of a "super-wavelet," a linear combination of wavelets that itself is treated as a wavelet. The super-wavelet allows the shape of the wavelet to adapt to a particular problem, which goes beyond adapting parameters of a fixed-shape wavelet. Simulations are given for 1-D signals, with the concepts extendable to imagery. Ideas are discussed for applying the concepts in the paper to phoneme and speaker recognition.

Neural network adaptive wavelets for signal representation and classification

A fast on-line automatic speaker/environment adaptation suitable for speech/speaker recognition system, method and computer program product are presented. The system comprises a computer system including a processor, a memory coupled with the processor, an input coupled with the processor for receiving acoustic signals, and an output coupled with the processor for outputting recognized words or sounds. The system includes a model-adaptation system and a recognition system, configured to accurately and efficiently recognize on-line distorted sounds or words spoken with different accents, in the presence of randomly changing environmental conditions. The model-adaptation system quickly adapts standard acoustic training models, available on audio recognition systems, by incorporating distortion parameters representative of the changing environmental conditions or the speaker's accent. By adapting models already available to the new environment, the system does not need separate adaptation training data.

Method and apparatus for fast on-line automatic speaker/environment adaptation for speech/speaker recognition in the presence of changing environments

The wavelet transform (WT), a time-scale repre- sentation, is linear by definition. However, the nonlinear en- ergy distribution of this transform is often used to represent the signal; it contains ''cross terms" which could cause prob- lems while analyzing multicomponent signals. In this paper, we show that the cross terms that exist in the energy distribution of the WT are comparable with those found in the Wigner dis- tribution (WD), a quadratic time-frequency representation, and the energy distribution of the short time Fourier transform (STFT), of closely spaced signals. The cross terms of the WT and the STFT energy distributions occur at the intersection of their respective WT and STFT spaces, while for the WD they occur midtime and midfrequency. The parameters of the cross terms are a function of the difference in center frequencies and center times of the perpended signals. The amplitude of these cross terms can be as large as twice the product of the magni- tudes of the transforms of the two signals in question in all three cases. In this paper, we consider the significance of the effect of the cross terms on the analysis of a multicomponent signal in each of these three representations. We also compare the advantages and disadvantages of all of these methods in appli- cations to signal processing.

http://ieeexplore.ieee.org/iel4/78/4076/00157292.pdf

Shubha Kadambe

Papers

Neural network adaptive wavelets for signal representation and classification

Method and apparatus for fast on-line automatic speaker/environment adaptation for speech/speaker recognition in the presence of changing environments

A Comparison of the Existence of "Cross Terms" in the Wigner Distribution and the Squared Magnitude of the Wavelet Transform and the Short Time

Spontaneous speech language identification with a knowledge of linguistics.

A pitch detector based on event detection using the dyadic wavelet tranform.