S. Narayan

Bio: S. Narayan is an academic researcher from HRL Laboratories. The author has contributed to research in topics: Filter (signal processing) & Linear filter. The author has an hindex of 2, co-authored 2 publications receiving 454 citations.

Transform domain LMS algorithm

Abstract: 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.

Finding the zeros of linear phase FIR frequency sampling digital filters

Abstract: The need for finding the zeros of FIR filters often arises in digital signal processing applications. If we want to convert linear phase finite-impulse response (FIR) filters into minimum phase FIR filters [1], [2], the problem of finding the zeros of the filter is encountered. This problem has been addressed in [3], but not solved. Here we introduce the idea of using frequency sampling filters for obtaining its zeros. The advantage of using this kind of filter resides in the fact that all the zeros on the unit circle are known. Furthermore, the zeros off the unit circle can be obtained with some simple processing. This type of filter is especially useful when high-order narrow-band filters are involved. Finding the zeros of very high-order equirriple low-pass filters can be a difficult, if not impossible, task.

Adaptive Filtering: Algorithms and Practical Implementation

Abstract: 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.

Frequency-domain and multirate adaptive filtering

Abstract: 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. >

Applications of digital signal processing to audio and acoustics

Abstract: 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.

Efficient least squares adaptive algorithms for FIR transversal filtering

Abstract: 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.

Analysis/synthesis-based microphone array speech enhancer with variable signal distortion

Abstract: A microphone array speech enhancement algorithm based on analysis/synthesis filtering that allows for variable signal distortion. The algorithm is used to suppress additive noise and interference. The processing structure consists of delaying the received signals so that the desired signal components add coherently, filtering each of the delayed signals through an analysis filter bank, summing the corresponding channel outputs from the sensors, applying a gain function to the channel outputs, and combining the weighted channel outputs using a synthesis filter. The structure uses two different gain functions, both of which are based on cross correlations of the channel signals from the two sensors. The first gain yields the GEQ-I array, which performs best for the case of a desired speech signal corrupted by uncorrelated white background noise. The second gain yields the GEQ-II array, which performs best for the case where there are more signals than microphones. The GEQ-II gain allows for a trade-off on a channel-dependent basis of additional signal degradation in exchange for additional noise and interference suppression.