Subband Adaptive Digital Filters Research Articles

Convergence behavior of the LMS algorithm in subband adaptive filtering

Subband adaptive filtering with an LMS-type algorithm has been considered as a possible alternative to the conventional fullband adaptive filtering. However, its convergence behavior has not yet been sufficiently analyzed. In this paper, an approximate expression for the convergence behavior of the cancellation error of the critically sampled subband adaptive digital filters (ADF) is presented. It is represented in the frequency domain and enables us to infer whether the subband ADF is efficient in terms of convergence speed. Computer simulations are presented to see the validity of the expression.

Spectral analysis of subband adaptive digital filters

Subband adaptive digital filters at stationary points are analyzed in the frequency domain. Approximate expressions for the optimal subband filters are presented, and then, a spectral representation of the error variance is derived. These are expressed in the frequency domain and, hence, enable us to see the aliasing effects in subband adaptive filtering.

On delayless subband adaptive filtering by subband/fullband transforms

Delayless subband adaptive digital filters (ADFs) have been proposed to avoid the drawbacks of the conventional subband ADF, which are transmission delay associated with the filterbank and degradation of the performance due to aliasing effects. In the delayless subband ADF, the tap-weight vectors are computed in subbands and then they are transformed into the tap-weight vector in fullband by a certain transform. In this letter, a general formulation for the delayless subband ADF is proposed, and some requirements on the fullband/subband transform are investigated.

Conditions for convergence of a delayless subband adaptive filter and its efficient implementation

This article considers the convergence characteristics of the delayless subband adaptive digital filters (ADFs) proposed by Morgan and Thi (see ibid., vol.43, p.1819-30, 1995). We derive a formula for the step-size parameter to ensure the convergence of ADPs and show that the derived formula enables self-adjusting of its value to ensure convergence regardless of the type of analysis filters employed or characteristics of the input signals. Consideration on the efficient implementation of the structure using short-length analysis filters is given through the results of simulations.

A subband adaptive filter with the statistically optimum analysis filter bank

Conventional subband adaptive digital filters using filter banks have shown degradation in their performance because of the nonideal nature of analysis filters. For this problem, we propose two kinds of subband adaptive digital filters. The first one has the statistically optimum analysis filter bank which minimizes error signals. This first method, however, needs some statistics of the signals; therefore, we propose another type of subband adaptive digital filter which requires no a priori knowledge of the statistics regarding the signals. Computer simulations are also given in order to show the efficiency of the proposed subband adaptive digital filters.