Analiza prijelazne pojave adaptivnih filtara primjenom općeg radnog okvira

Employing a recently introduced framework in which a large number of adaptive filter algorithms can be viewed as special cases, we present a generalized transient analysis. An important implication of this is that while the theoretical analysis is performed for a generic filter coefficient update equation the results are directly applicable to a large range of adaptive filter algorithms simply by specifying some parameters of this generic filter coefficient update equation. In particular we point out that theoretical learning curves for the Least Mean Square (LMS), Normalized Least Mean Square (NLMS), the Affine Projection Algorithm (APA) and its relatives, as well as the Recursive Least Squares (RLS) algorithm are obtained as special cases of a general result. Subsequently, the recently introduced Fast Euclidian Direction Search (FEDS) algorithms as well as the Pradhan-Reddy subband adaptive filter (PRSAF) are used as non-trivial examples when we demonstrate the usefulness and versatility of the proposed approach to adaptive filter transient analysis through an experimental evaluation.U radu se predstavlja poopćena analiza prijelaznih pojava adaptivnih filtara, koja se zasniva na primjeni nedavno predstavljenog radnog okvira koji velik broj raznih algoritama adaptivnih filtara promatra kao specijalne slučajeve. Važna posljedica toga je da su rezultati, iako se teoretska analiza provodi na generičkoj jednadžbi za osvježavanje koeficijenta filtra, izravno primjenjivi na razne algoritme adaptivnih filtara jednostavnom specificikacijom nekih parametara generičke jednadžbe za osvježavanje koeficijenata filtra. Posebno se naglašava da su teoretske krivulje učenja za algoritam najmanjih kvadrata (LMS), normalizirani algoritam najmanjih kvadrata (NLMS), afini projekcijski algoritam (APA) i njemu srodnih algoritama, kao i za rekurzivni algoritam najmanjih kvadrata (RLS) dobivene kao posebni slučajevi poopćenog rješenja. Potom se nedavno predstavljeni algoritmi brze euklidske usmjerene pretrage (FEDS) te Pradhan-Reddy pojasni adaptivni filtar (PRSAF) koriste kao netrivijalni primjeri za dokazivanje korisnosti i univerzalnosti predloženog pristupa analizi prijelaznih pojava adaptivnih filtara kroz eksperimentalnu evaluaciju

Interference cancellation in respiratory sounds via a multiresolution joint time-delay and signal-estimation scheme

Includes bibliographical references.This paper is concerned with the problem of cancellation of heart sounds from the acquired respiratory sounds using a new joint time-delay and signal-estimation (JTDSE) procedure. Multiresolution discrete wavelet transform (DWT) is first applied to decompose the signals into several subbands. To accurately separate the heart sounds from the acquired respiratory sounds, time-delay estimation (TDE) is performed iteratively in each subband using two adaptation mechanisms that minimize the sum of squared errors between these signals. The time delay is updated using a nonlinear adaptation, namely the Levenberg-Marquardt (LM) algorithm, while the function of the other adaptive system-which uses the block fast transversal filter (BFTF)—is to minimize the mean squared error between the outputs of the delay estimator and the adaptive filter. The proposed methodology possesses a number of key benefits such as the incorporation of multiple complementary information at different subbands, robustness in presence of noise, and accuracy in TDE. The scheme is applied to several cases of simulated and actual respiratory sounds under different conditions and the results are compared with those of the standard adaptive filtering. The results showed the promise of the scheme for the TDE and subsequent interference cancellation

Analiza prijelazne pojave adaptivnih filtara primjenom općeg radnog okvira

Employing a recently introduced framework in which a large number of adaptive filter algorithms can be viewed as special cases, we present a generalized transient analysis. An important implication of this is that while the theoretical analysis is performed for a generic filter coefficient update equation the results are directly applicable to a large range of adaptive filter algorithms simply by specifying some parameters of this generic filter coefficient update equation. In particular we point out that theoretical learning curves for the Least Mean Square (LMS), Normalized Least Mean Square (NLMS), the Affine Projection Algorithm (APA) and its relatives, as well as the Recursive Least Squares (RLS) algorithm are obtained as special cases of a general result. Subsequently, the recently introduced Fast Euclidian Direction Search (FEDS) algorithms as well as the Pradhan-Reddy subband adaptive filter (PRSAF) are used as non-trivial examples when we demonstrate the usefulness and versatility of the proposed approach to adaptive filter transient analysis through an experimental evaluation.U radu se predstavlja poopćena analiza prijelaznih pojava adaptivnih filtara, koja se zasniva na primjeni nedavno predstavljenog radnog okvira koji velik broj raznih algoritama adaptivnih filtara promatra kao specijalne slučajeve. Važna posljedica toga je da su rezultati, iako se teoretska analiza provodi na generičkoj jednadžbi za osvježavanje koeficijenta filtra, izravno primjenjivi na razne algoritme adaptivnih filtara jednostavnom specificikacijom nekih parametara generičke jednadžbe za osvježavanje koeficijenata filtra. Posebno se naglašava da su teoretske krivulje učenja za algoritam najmanjih kvadrata (LMS), normalizirani algoritam najmanjih kvadrata (NLMS), afini projekcijski algoritam (APA) i njemu srodnih algoritama, kao i za rekurzivni algoritam najmanjih kvadrata (RLS) dobivene kao posebni slučajevi poopćenog rješenja. Potom se nedavno predstavljeni algoritmi brze euklidske usmjerene pretrage (FEDS) te Pradhan-Reddy pojasni adaptivni filtar (PRSAF) koriste kao netrivijalni primjeri za dokazivanje korisnosti i univerzalnosti predloženog pristupa analizi prijelaznih pojava adaptivnih filtara kroz eksperimentalnu evaluaciju

New Directions in Subband Coding

Two very different subband coders are described. The first is a modified dynamic bit-allocation-subband coder (D-SBC) designed for variable rate coding situations and easily adaptable to noisy channel environments. It can operate at rates as low as 12 kb/s and still give good quality speech. The second coder is a 16-kb/s waveform coder, based on a combination of subband coding and vector quantization (VQ-SBC). The key feature of this coder is its short coding delay, which makes it suitable for real-time communication networks. The speech quality of both coders has been enhanced by adaptive postfiltering. The coders have been implemented on a single AT&T DSP32 signal processo

Residual echo signal in critically sampled subband acoustic echo cancellers based on IIR and FIR filter banks

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Perceptual models in speech quality assessment and coding

The ever-increasing demand for good communications/toll quality speech has created a renewed interest into the perceptual impact of rate compression. Two general areas are investigated in this work, namely speech quality assessment and speech coding. In the field of speech quality assessment, a model is developed which simulates the processing stages of the peripheral auditory system. At the output of the model a "running" auditory spectrum is obtained. This represents the auditory (spectral) equivalent of any acoustic sound such as speech. Auditory spectra from coded speech segments serve as inputs to a second model. This model simulates the information centre in the brain which performs the speech quality assessment. [Continues.

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications