First published in 1995, Wavelets and Subband Coding offered a unified view of the exciting field of wavelets and their discrete-time cousins, filter banks, or subband coding. The book developed the theory in both continuous and discrete time, and presented important applications. During the past decade, it filled a useful need in explaining a new view of signal processing based on flexible time-frequency analysis and its applications. Since 2007, the authors now retain the copyright and allow open access to the book.

Wavelets and Subband Coding

A system is described in which a bank of bandpass filters is used to analyze signals. Filter characteristics are chosen to match the empirical critical bands of human auditory perception. Each bandpass filter is followed by a detector. A Generalized Short-Time Fourier Transform (GSTFT) magnitude analysis is used to implement the Filter/Detector bank. An efficient recursive implementation is described. Synthesis equations are presented which allow signal reconstruction from the GSTFT magnitude. An example is given in which a sinusoidal signal is analyzed and synthesized by this method.

Speech analysis/synthesis based on perceptin

A complex sequence can be recovered to within an overall constant phase factor from its generalized short-time Fourier transform magnitude, which includes the wavelet scalogram and short-time Fourier transform magnitude as special cases. >

Complex signal reconstruction from time-frequency magnitude

Materialistic philosophy opens out universal law of things evolution. Chinese Yinyang theory is materialistic. Wavelet contain dialectic rule inevitably. As material mathematic technique, wavelet can be given a new look by Chinese archaian dialectics.

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Consideration on Wavelet through Chinese Archaian Dialectics

The wavelet transform is a special case of the generalized short-time Fourier transform (GSTFT), which predates wavelet analysis by many years. The theorem presented here is one example of how GSTFT research can be beneficially applied to wavelet analysis. >

J.C. Anderson

Papers

A wavelet magnitude analysis theorem