Yoshito Higa

A subband adaptive filter with the optimum analysis filter bank

Conventional subband ADFs (adaptive digital filters) using filter banks have shown degradation in performance because of the non-ideal nature of filters. For this problem, we propose a new type of subband ADF incorporating two types of analysis filter bank. In this paper, we show that we can design the optimum filter bank which minimizes the LMSE (least mean squared error). In other words, we can design a subband ADF with less MSE than that of conventional subband ADFs.

A subband adaptive filter with two types of analysis filter bank

Conventional subband ADFs (adaptive digital filters) using filter banks have shown degradation in performance because of the non-ideal nature of filters. For this problem, we propose a new type of subband ADF incorporating two types of analyses filter bank. We show that we can design the optimum filter bank which minimizes the LMSE (least mean squared error). In other words, we can design a subband ADF with less MSE than that of conventional subband ADFs.<<ETX>>

An over-sampling subband adaptive filter with the optimal real filter bank

The over-sampling subband ADFs are in high demand because of shortening the convergence of adaptation algorithms and being able to avoid aliasing. However, the conventional methods must use the complex coefficient filter banks, which require a lot of computation compared to the real filter banks. The aim of this correspondence is to develop a new oversampling subband ADF which does not require complex coefficient filter banks but real filter banks. The filter banks are optimized such that the mean square error can be minimized. It turns out that the proposed scheme can minimize the aliasing effect due to decimation.

A subband adaptive filter with a variable analysis filter bank

Conventional subband adaptive digital filters (ADFs) using filter banks have shown performance degradation because of the non-ideal nature of the analysis filters. For this problem, we propose a new class of subband ADFs which has two kinds of analysis filter banks. We first claim that subband ADFs with the optimum analysis filter bank, which can minimize the least squares error at each bin, can be designed in accordance with the statistics of the signals. We also propose a practical type of subband ADF which requires no a priori knowledge of the the signals statistics. Several computer simulations are also given in order to show the efficiency of the proposed subband adaptive digital filters.

A gradient type algorithm for blind system identification based on second order statistics

In this paper, we propose a new structure of blind system identification and its cost function. The proposed cost function is a quadratic form and has the unique solution. In addition, the proposed scheme can employ iterative algorithms which achieve less computational complexity and can be easily realized in real time processing. In order to verify the effectiveness of the proposed scheme, a computer simulation has been shown.