This paper relates to a novel algorithm for fast estimation of the coefficients of the adaptive FIR filter. The novel algorithm is derived from a first order IIR filter experssion clarifying the estimation process of the NLMS (normalized least mean square) algorithm. The expression shows that the estimation process is equivalent to a procedure extracting the cross-correlation coefficient between the input and the output of an unknown system to be estimated. The interpretation allows to move a subtraction of the echo replica beyond the IIR filter, and the movement gives a construction with the IIR filter coefficient of unity which forms the arithmetic mean. The construction in comparison with the conventional NLMS algorithm, improves the covergence rate extreamly. Moreover, when we use the construction with a simple technique which limits the term of calculating the correlation coefficient in the beginning of a convergence process, the convergence delay becomes negligible. This is a very desirable performance for acoustic echo canceller. In this paper, double-talk and echo path fluctuation are also studied as the first stage for application to acoustic echo canceller. The two subjects can be resolved by introducing two switches and delays into the evaluation process of the correlation coefficient.

This letter presents a new algorithm for echo cancellers, which prevents the reduction of echo return loss due to a double-talk. The essence of the algorithm is to introduce signal delays to avoid the reduction. A convergence condition in the algorithm was examined by using the IIR filter expression of the NLMS algorithm, and it was concluded that the IIR filter should be a low pass filter with unity gain. The condition is accomplished by selecting a small step gain.

This paper introduces an acoustic echo canceller system materialized with a 16-bit fixed point processing type DSP (Analog Devices, ADSP-2181). This experimental system uses the tri-quantized-x individually normalized least mean square (INLMS) algorithm little degrading the convergence property under the fixed point processing. The experimental system also applies a small step gain to the algorithm to prevent the double-talk from increasing the estimation error. Such a small step gain naturally reduces the convergence speed. The experimental system compensates the reduction by applying the block length adjustment technique to the algorithm. This technique enables to ceaselessly update the coefficients of the adaptive filter even when the reference signal power is low. The experimental system thus keeps the echo return loss enhancement (ERLE) high against the double-talk.

A carbon granule microphone with carbonaceous electrodes, which is lower in cost and more convenient for mass production than the conventional microphone with gold plated electrodes, was studied as a telephone microphone. A molded piece made from a mixture of synthetic resin and carbon powder can be used for the back electrode and paint made from it can be used to coat the dome electrode. A new microphone, whose sensitivity is as high as a conventional microphone, shows higher resistance. This means that the efficiency of the new microphone seems to be slightly less. However, the sensitivity change in the new microphone under excitation by voice is less than that of the conventional microphone. The new microphone sensitivity dependence on DC input power is almost the same as that of a conventional microphone.

When we use a telephone-handset, the frequency response of the telephone-earphone becomes degraded because of the leak through the slit between the ear and the earphone. Consequently, it is very important to establish the design method of the telephone-handset which reduces the effect of leak. No one has tried to design the telephone-handset to reduce the effect. We are the only ones to have proposed an automatic design method by nonlinear optimization techniques. However, this method gives only one set of the acoustic parameters aiming at a certain specific target frequency response, and therefore lacks flexibility in the actual design problem. On the other hand, the design method proposed in this paper, which uses Monte-Carlo method, gives an infinite number of sets of acoustic parameters that realize infinite frequency responses within the target allowable region. As these infinite number of sets become directly the design ranges of acoustic parameters, the proposed method has the flexibility that any set of the acoustic parameters belonging to the design ranges guarantees the corresponding response to be within the target allowable region, and at the same time reduces the effect of leak. This flexibility is advantageous to the actual design problem.

The filtered-x algorithm, which is widely applied to active noise control system, requires setting a small step gain. Such a small step gain reduces the noise reduction effect when the alogrithm is implemented by fixed point processing. This paper presents an experimental result that the 'polarized-g' individually normalized least mean square (INLMS) algorithm can provide almost the same noise reduction effect even in the fixed point processing of 16 bits as that in floating point processing.

This paper presents an equation capable of briefly evaluating the length of white noise sequence to be sent as a training signal. The equation is formulated by utilizing the formula describing the convergence property, which has been derived from the IIR filter expression of the NLMS algorithm. The result revealed that the length is directly proportional to I/[K(2-K)] where K is a step gain and I is the number of the adaptive filter taps.

The individually normalized least mean square (INLMS) algorithm is proposed as an adaptive algorithm suitable for the fixed point processing. The convergence property of the INLMS algorithm, however, is not yet analyzed enough. This paper first derives an equation describing the convergence property by exploiting the technique of expressing the INLMS algorithm as a first order infinite impulse response (IIR) filter. According to the equation derived thus, the decreasing process of the estimation error is represented as the response of another IIR filter expression. By using the representation, this paper second derives the convergence condition of the INLMS algorithm as the range of the step size making a low path filter of the latter IIR filter. This paper also derives the step size maximizing the convergence speed as the maximum coefficient of the latter IIR filter and finally clarifies the range of the step size recommended in the practical system design.