From the Publisher:
Adaptive Filtering: Algorithms and Practical Implementation is a concise presentation of adaptive filtering, covering as many algorithms as possible while avoiding adapting notations and derivations related to the different algorithms. Furthermore, the book points out the algorithms which really work in a finite-precision implementation, and provides easy access to the working algorithms for the practicing engineer. Adaptive Filtering: Algorithms and Practical Implementation may be used as the principle text for courses on the subject, and serves as an excellent reference for professional engineers and researchers in the field.

Adaptive Filtering: Algorithms and Practical Implementation

An overview is presented of several frequency-domain adaptive filters that efficiently process discrete-time signals using block and multirate filtering techniques. These algorithms implement a linear convolution that is equivalent to a block time-domain adaptive filter, or they generate a circular convolution that is an approximation. Both approaches exploit the computational advantages of the FFT. Subband adaptive filtering is also briefly described. Here the input data are first processed by a bank of narrowband bandpass filters that are approximately nonoverlapping. The transformed signals are then decimated by a factor that depends on the degree of aliasing that can be tolerated, resulting in a large computational savings. Several performance issues are considered, including convergence properties and computational complexities of the adaptive algorithms and the effects of circular convolution and aliasing on the converged filter coefficients. >

Frequency-domain and multirate adaptive filtering

A new, oddly stacked, critically sampled, single side-band (SSB) [7] analysis/synthesis system based on Time Domain Aliasing Cancellation (TDAC) [1],[2] is described in this paper. The specifications for the analysis and synthesis filter responses are developed and a number of designs which satisfy the reconstruction requirements are described. The application of TDAC systems to Subband/Transform coding is also discussed and the objective performance of a 32 band coder using several different window designs is presented and compared with a coder based on Frequency Domain Aliasing Cancellation (FDAC) filter banks [3]-[5].

Subband/Transform coding using filter bank designs based on time domain aliasing cancellation

With the advent of `multimedia', digital signal processing (DSP) of sound has emerged from the shadow of bandwidth limited speech processing to become a research field of its own. To date, most research in DSP applied to sound has been concentrated on speech, which is bandwidth limited to about 4 kilohertz. Speech processing is also limited by the low fidelity typically expected in the telephone network. Today, the main applications of audio DSP are high quality audio coding and the digital generation and manipulation of music signals. They share common research topics including perceptual measurement techniques and analysis/synthesis methods. Additional important topics are hearing aids using signal processing technology and hardware architectures for digital signal processing of audio. In all these areas the last decade has seen a significant amount of application-oriented research. The frequency range of wideband audio has an upper limit of 20 kilohertz and the resulting difference in frequency range and Signal to Noise Ratio (SNR) due to sample size must be taken into account when designing DSP algorithms. There are whole classes of algorithms that the speech community is not interested in pursuing or using. These algorithms and techniques are revealed in this book. This book is suitable for advanced level courses and serves as a valuable reference for researchers in the field. Interested and informed engineers will also find the book useful in their work.

Applications of digital signal processing to audio and acoustics

A unified view of algorithms for adaptive transversal FIR filtering and system identification has been presented. Wiener filtering and stochastic approximation are the origins from which all the algorithms have been derived, via a suitable choice of iterative optimization schemes and appropriate design parameters. Following this philosophy, the LMS algorithm and its offspring have been presented and interpreted as stochastic approximations of iterative deterministic steepest descent optimization schemes. On the other hand, the RLS and the quasi-RLS algorithms, like the quasi-Newton, the FNTN, and the affine projection algorithm, have been derived as stochastic approximations of iterative deterministic Newton and quasi-Newton methods. Fast implementations of these methods have been discussed. Block-adaptive, and block-exact adaptive filtering have also been considered. The performance of the adaptive algorithms has been demonstrated by computer simulations.

Efficient least squares adaptive algorithms for FIR transversal filtering

The concept of transform domain adaptive filtering is introduced. In certain applications, filtering in the transform domain results in great improvements in convergence rate over the conventional time-domain adaptive filtering. The relationship between several existing frequency domain adaptive filtering algorithms is established. Applications of the discrete Fourier transform (DFT) and the discrete cosine transform (DCT) domain adaptive filtering algorithms in the areas of speech processing and adaptive line enhancers are discussed.

Transform domain LMS algorithm

A digital bandpass filter-bank for demodulating a wideband frequency multiplexed signal into a specified number of uniform narrow band channel outputs, and conversely for modulating a set of channel inputs into a composite frequency multiplexed signal, can be realized efficiently by the combination of a suitable transform processor and a weighting network. The design and implementation of these filter-banks for different types of input-ouput configurations such as complex signals at both the wideband and narrow band sides, real signal at the wideband side and complex signals at the narrow band side, and real signals at both ends are discussed. Applications in telecommunications (TDM-FDM translators), speech processing (phase vocoders), signal analysis and detection (spectrum analyzers) and other signal processing systems are also described.

Design and applications of uniform digital bandpass filter banks

A complete solution is given to the problem of finding the number of distinct quadratic residues for a composite modulus. Two specific applications of this result are described. The first one concerns the efficient implementation of chirp filters. It is shown that by an optimum choice of the number of taps, the number of multiplications required to realize a transversal chirp filter can be greatly reduced. Secondly, an algorithm for the computation of DFT, based on chirp filtering, is discussed. It has the potential of being faster than the FFT in certain cases and, in addition, requires less storage for the sine-cosine values.

Quadratic residues: Application to chirp filters and discrete Fourier transforms

Time-domain methods for the design of recursive digital filters using a squared error criterion are compared with a frequency-domain technique. Levy's method, which has been used to estimate transfer functions of continuous-time systems is modified to obtain design equations for digital filters. A special case of Levy's method is shown to be essentially equivalent to the time-domain methods.

On least-squars design of recursive digital filters

By initially transforming a signal into its Arcsine value, the multiplications required in the subsequent processing of the signal can be replaced by additions and table look-ups. With the advent of large read-only memories, this may be an attractive method to reduce computation time and simplify hardware in signal processing systems. An elegant method of obtaining the Arcsine transform and its application to several important signal processing problems are discussed. Among these are computation of discrete Fourier transforms and correlation functions, realization of digital filters, modulation and detection of signals, and construction of frequency synthesizers.

M. Narasimha

Papers

Transform domain LMS algorithm

Design and applications of uniform digital bandpass filter banks

Quadratic residues: Application to chirp filters and discrete Fourier transforms

On least-squars design of recursive digital filters

The Arcsine transform and its applications in signal processing