An automatic tap assignment method in sub-band adaptive filter is proposed in this letter. The number of taps of the adaptive filter in each band is controlled by the mean-squared error. The numbers of taps increase in the bands which have large errors, while they decrease in the bands having small errors, until residual errors in all the bands become the same. In this way, the number of taps in a band is roughly proportional to the length of the impulse response of the unknown system in this band. The convergence rate and the residual error are improved, in comparison with existing uniform tap assignment. Effectiveness of the proposed method has been confirmed through computer simulation.

This paper presents a new structure for noise and echo cancelers based on a combined fast abaptive algorithm. The main purpose of the new structure is to detect both the double-talk and the unknown path change. This goal is accomplished by using two adaptive filters. A main adaptive filter Fn, adjusted only in the non-double-talk period by the normalized LMS algorithm, is used for providing the canceler output. An auxiliary adaptive filter Ff, adjusted by the fast RLS algorithm, is used for detecting the double-talk and obtaining a near optimum tap-weight vector for Fn in the initialization period and whenever the unknown path has a sudden or fast change. The proposed structure is examined through computer simulation on a noise cancellation problem. Good cancellation performance and stable operation are obtained when signal is a speech corrupted by a white noise, a colored noise and another speech signal. Simulation results also show that the proposed structure is capable of distinguishing the near-end signal from the noise path change and quickly tracking this change.

For system identification problems, such as noise and echo cancellation, FIR adaptive filters are mainly used for their simple adaptation and numerical stability. When the unknown system is a high-Q resonant system, having a very long impulse response, IIR adaptive filters are more efficient for reduction in the order of a transfer function. One way to realize the IIR adaptive filter is a separate form, in which the numerator and the denominator are separately realized and adjusted. In the actual applications, the order of the unknown system is not known. In this case, it is very important to estimate the total order and the order assignment on the numerator and the denominator. In this paper, effects of the order estimation error on the residual error are investigated. In this form, indirect error evaluation called "equation error" is used. Through theoretical and numerical investigation, the following results are obtained. First, under estimation of the order of the denominator causes large degradation. Second, over estimation can improve the performance. However, this improvement is saturated to some extent due to cancellation of the redundant poles and zeros. Third, the system identification error is proportional to the equation error as the adaptive filter approaching the optimum. Finally, there is possibility of recovering from the unstable state as the order assignment approaches to the optimum in an adaptive process using the equation error. Computer solutions are provided to aid in gaining insight of the order assignment and stability problem.

Single-reference and multi-reference noise canceller (SRNC and MRNC) performances are investigated based on correlation between signal and noise. Exact relations between these noise canceller performances and signal-noise correlation have not been well discussed yet. In this paper, the above relations are investigated based on theoretical, analysis and computer simulation. The normalized LMS (NLMS) algorithm is employed. Uncorrelate, partially correlated, and correlated signal and noise combinations are taken into account. Computer simulation is carried out, using real speech, white noise, real noise sound, sine wave signals, and their combinations. In the SRNC problem, spectral analysis is applied to derive the canceller output power spectrum. From the simulation results, it is proven that the SRNC performance is inversely proportional to the signal-noise correlation as expected by the theoretical analysis. From the simulation results, the MRNC performance is more sensitive to the signal-noise correlation than that of SRNC. When the signal-noise correlation is high, by using a larger number of adaptive filter taps, the noise is reduced more, and, the signal distortion is increased. This means the signal components included in the noise are canceled exactly.