Rogerio Guedes Alves

New structures for adaptive filtering in subbands with critical sampling

Some properties of an adaptive filtering structure that employs an analysis filterbank to decompose the input signal and sparse adaptive filters in the subbands are investigated in this paper. The necessary conditions on the filterbank and on the structure parameters for exact modeling of an arbitrary linear system with finite impulse response (FIR) are derived. Then, based on the results obtained for the sparse subfilter structure, a new family of adaptive structures with critical sampling of the subband signals, which can also yield exact modeling, is obtained. Two adaptation algorithms based on the normalized LMS algorithm are derived for the new subband structures with critical sampling. A convergence analysis, as well as a computational complexity analysis, of the proposed adaptive structures are presented. The convergence behavior of the proposed adaptive structures is verified by computer simulations and compared with the behavior of previously proposed algorithms.

New results on adaptive filtering using filter banks

The properties of an adaptive filtering structure which employs an analysis filter bank to decompose the input signal and sparse adaptive filters in the subbands are investigated in this paper. The necessary conditions on the filter bank and on the structure parameters for exact modeling of an arbitrary linear system with finite impulse response (FIR) are derived. Then, based on the results obtained for the sparse subfilter structure, a new family of adaptive structures with critical sampling of the subband signals, which can also yield exact modelization, is obtained. Computer simulations are presented to illustrate the convergence behavior of the adaptive subband structures investigated in the paper.

Convergence analysis of an oversampled subband adaptive filtering structure with local errors

Subband adaptive filtering has been recently studied by a large number of researchers. The main alternatives are structures with critical sampling and noncritical sampling, and structures that use local errors and global error in the adaptive algorithm. In this paper a theoretical convergence analysis for the case of an oversampled subband adaptive altering structure with local errors is presented. The convergence rate and misadjustment of the algorithm can be estimated from the results of this analysis. Computer simulations are presented to illustrate the convergence behavior of the subband adaptive algorithm and to verify the theoretical results.

A new adaptive subband structure with critical sampling

In this paper, a new adaptive subband structure with critical sampling of the subband signals, which yields exact modeling of FIR systems, is derived. An adaptation algorithm, which minimizes the sum of the subband squared-errors, is obtained for the updating of the coefficients of the new subband structure, resulting in significant convergence rate improvement for colored input signals when compared to the full-band LMS algorithm. A simplified version of the adaptation algorithm, with reduced computational complexity, is also presented. An efficient implementation of the proposed subband structure is described, with computational savings of the order of the number of subbands when compared to the full-band LMS. Computer simulations illustrate the convergence behavior of the proposed algorithms.

A new critically sampled non-uniform subband adaptive structure

A non-uniform subband adaptive structure with critical sampling of the subband signals is derived. With the assumption of non-overlapping between non-adjacent analysis filters, the resulting structure yields exact modeling of an arbitrary FIR system. An LMS-type adaptation algorithm, which minimizes the sum of the subband squared-errors, is obtained for updating the coefficients of the proposed structure, resulting in significant convergence rate improvement for colored input signals when compared to the fullband LMS algorithm. Computer simulations illustrate the convergence behavior of the proposed non-uniform subband adaptive filter in the applications of system identification and acoustic echo cancellation.

Subband adaptive filtering with critical sampling using the data selective affine projection algorithm

Subband adaptive filtering techniques have been recently developed for a number of applications, such as acoustic echo cancellation and wideband active noise control. Such applications require adaptive filters with hundreds of taps, resulting in high computational complexity and low convergence rate for LMS based algorithms. For fullband system, a variety of adaptive algorithm, which improve the adaptation convergence rate, have been developed. Most of them (such as the affine projection algorithm), however, present larger complexity than the conventional LMS algorithm. Such computational load can be reduced by making use of subband processing techniques. Considering these matters, we apply the affine projection algorithm (APA) in a recently proposed subband adaptive filter structure.

A new open loop delayless subband adaptive filter structure

Subband techniques have been recently developed for adaptive filters, since some of the applications such as acoustic echo cancellation and wideband active noise control need adaptive filters with hundreds of taps, which result in high computational complexity and low convergence rate. By the use of subband adaptive algorithms, the computational complexity may be reduced along with convergence rate; however, a delay is introduced in the signal path. To remove the delay, the delayless subband adaptive filter architecture with both the open loop and the closed loop structures have been introduced. This paper presents a new open loop delayless subband adaptive filter structure with critical sampling, where the performance concerning the mean square error of the subband adaptive algorithm, caused due to the aliasing existing in the subband structure, is superior to the results obtained up to now for open loop delayless structures.

Robust speaker verification in colored noise environment

Noise robustness of automatic speaker verification systems is crucial in real life applications. A study on the performance of several spectral subtraction-based speech enhancement techniques shows the poor performance of these algorithms when used as a preprocessing stage of the speaker verification system in the presence of colored noise. In this paper, a new technique based on the addition of modeled colored noise is introduced. Experimental results in both white and colored noise environments are presented, showing the improvement of the proposed scheme.

Performance comparison of adaptive subband structures applied to acoustic echo cancelling

Subband adaptive filtering has been recently studied by a large number of researchers. Subband structures with non-critical sampling of the subband signals have been frequently employed in order to avoid aliasing effects. Recently, new subband structures with critical sampling have been developed in which the aliasing between adjacent subbands is completely cancelled. In this paper, the convergence behavior and the computational complexity of oversampled and critically sampled subband structures are compared for acoustic echo cancelling application. A theoretical convergence analysis is presented, from which the convergence rates can be estimated.

Convergence analysis of a new subband adaptive structure with critical sampling

In this paper, we present a convergence analysis of the subband adaptive filter structure with critical sampling recently proposed by the authors. This new adaptive subband structure yields exact modeling of FIR systems and leads to computational complexity savings of the order of the number of subbands. Two adaptation algorithms are investigated for the updating of the adaptive coefficients of this new subband structure, and a theoretical analysis of their convergence rates and misadjustments is derived. Computer simulations are presented in order to verify the performance of the proposed algorithms and the theoretical results.