This paper proposes a new hybrid active noise control (ANC) system without the estimation of the secondary path filter in advance. The algorithm of the feedforward part of the proposed method is based on the simultaneous equations method and the feedback part employs the filtered-X LMS algorithm. The estimation of the secondary path filter is obtained in the operation of the feedforward part and it is used in the feedback part. When the secondary path changes in the operation of the system, the proposed system can follow to this change. In the simulation example which treats the colored measurement noise, the fine noise reduction performance is obtained.

The normalized least mean square (NLMS) algorithm has the drawback that the convergence speed of adaptive filter coefficients decreases when the reference signal has high auto-correlation. A technique to improve the convergence speed is to apply the decorrelated reference signal to the calculation of the gradient defined in the NLMS algorithm. So far, only the effect of the improvement is experimentally examined. The convergence property of the adaptive algorithm to which the technique is applied is not analized yet enough. This paper first defines a cost function properly representing the criterion to estimate the coefficients of adaptive filter. The name given in this paper to the adaptive algorithm exploiting the decorrelated reference signal, 'normalized least mean EE' algorithm, exactly expresses the criterion. This adaptive algorithm estimates the coefficients so as to minimize the product of E and E' that are the differences between the responses of the unknown system and the adaptive filter to the original and the decorrelated reference signals, respectively. By using the cost function, this paper second specifies the convergence condition of the normalized least mean EE' algorithm and finally presents computer simulations, which are calculated using real speech signal, to demonstrate the validity of the convergence condition.

The adaptive system design by 16-bit fixed point processing enables to employ an inexpensive digital signal processor (DSP). The narrow dynamic range of such 16 bits, however, does not guarantee the same performance that is confirmed beforehand by computer simulations. A cause of degrading the performance originates in the operation halving the word length doubled by multiplication. This operation rounds off small signals staying in the lower half of the doubled word length to zero. This problem can be solved by limiting the multiplier to only its sign () like the signed regressor algorithm, named 'bi-quantized-x' algorithm in this paper, for the convenience mentioned below. This paper first derives the equation describing the convergence property provided by a type of signed regressor algorithms, the bi-quantized-x normalized least mean square (NLMS) algorithm, and then formulates its convergence condition and the step size maximizing the convergence rate. This paper second presents a technique to improve the convergence property. The bi-qiantized-x NLMS algorithm quantizes the reference signal to 1 according to the sign of the reference signal, whereas the technique moreover assigns zero to the reference signal whose amplitude is less than a predetermined level. This paper explains the principle that the 'tri-qunatized-x' NLMS algorithm employing the technique can improve the convergence property, and confirms the improvement effect by computer simulations.

In this paper, we propose a flexible gaze detection method using single PTZ (Pan-Tilt-Zoom) camera. In this method, a PTZ camera traces user's face and measures position of their viewing point, so they do not need to fix their head in front of camera. Furthermore, to realize accurate gaze detection, we employ elliptical iris template matching. To reduce calculation amount of iris template matching, we get rough gaze direction by simple method on ahead to decide ellipse shape. In this paper, we also adapt to variation of facial orientations, which will affect to detect viewing position and gaze direction. After several experiments, we examine accuracy of gaze detection and head tracking ability of this system.

We have proposed a noise reduction method based on a noise reconstruction system (NRS). The NRS uses a linear prediction error filter (LPEF) and a noise reconstruction filter (NRF) which estimates background noise by system identification. In case a fixed step size for updating tap coefficients of the NRF is used, it is difficult to reduce background noise while maintaining the high quality of enhanced speech. In order to solve the problem, a variable step size is proposed. It makes use of cross-correlation between an input signal and an enhanced speech signal. In a speech section, a variable step size becomes small so as not to estimate speech, on the other hand, large to track the background noise in a non-speech section.

In this paper, we propose a normalization method dividing the gradient vector by the sum of the diagonal and two adjoining elements of the matrix expressing the correlation between the components of the discrete Fourier transform (DFT) of the reference signal used for the identification of unknown system. The proposed method can thereby improve the estimation speed of coefficients of adaptive filter.

In this paper, we propose a method capable of shortening the distance from a noise detection microphone to a loudspeaker in active noise control system with non-minimum phase secondary path. The distance can be basically shortened by forming the noise control filter, which produces the secondary noise provided by the loudspeaker, with the cascade connection of a non-recursive filter and a recursive filter. The output of the recursive filter, however, diverges even when the secondary path includes only a minimum phase component. In this paper, we prevent the divergence by utilizing MINT (multi-input/output inverse theorem) method increasing the number of secondary paths than that of primary paths. MINT method, however, requires a large scale inverse matrix operation, which increases the processing cost. We hence propose a method reducing the processing cost. Actually, MINT method has only to be applied to the non-minimum phase components of the secondary paths. We hence extract the non-minimum phase components and then apply MINT method only to those. The order of the inverse matrix thereby decreases and the processing cost can be reduced. We finally show a simulation result demonstrating that the proposed method successfully works.

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.

A multiple channel active noise control (ANC) system with several secondary sources, error sensors, and reference sensors has been used for complicated noise fields. Centralized multiple channel ANC systems have been proposed, however implementation of such systems becomes difficult according to increase of control points. Distributed multiple channel ANC systems which have more than a controller are considered. This paper proposes a new implementation of distributed multiple channel ANC systems based on simultaneous equations methods. In the proposed algorithm, communications between controllers are permitted to distribute the computational burden and to improve the performance of noise reduction. This algorithm shows good performances for noise cancellation and tracking of changes in the error paths.

The signed regressor algorithm, a variation of the least mean square (LMS) algorithm, is characterized by the estimation way of using the clipped reference signals, namely, its sign (). This clipping, equivalent to quantizing the reference signal to 1, only increases the estimation error by about 2 dB. This paper proposes to increase the number of the quantization steps to three, namely, 1 and 0, and shows that the 'tri-quantized-x' normalized least mean square (NLMS) algorithm with three quantization steps improves the convergence property.

A noise reduction technique to reduce wideband and sinusoidal noise in a noisy speech is proposed. In an actual environment, background noise includes not only wideband noise but also sinusoidal noise, such as ventilation fan and engine noise. In this paper, we propose a new noise reduction system which uses two types of adaptive line enhancers (ALE) and a noise estimation filter (NEF). First, the two ALEs are used to estimate speech components. The first ALE is used to reduce sinusoidal noise superposed on speech and wideband noise, while the second ALE is used to reduce wideband noise superposed on speech. However, since the quality of the speech enhanced by two ALEs is not good enough due to the difficulty in estimating unvoiced sound using the two ALEs, the NEF is used to improve on noise reduction capability. The NEF accurately estimates the background noise from the signal occupied by noise components, which is obtained by subtracting the speech enhanced by two ALEs from noisy speech. The enhanced speech is obtained by subtracting the estimated noise from noisy speech. Furthermore, the noise reduction system with feedback path is proposed to improve further the quality of enhanced speech.

A noise reduction technique to reduce background noise in noisy speech is proposed. We have proposed the noise reduction method which uses a noise reconstruction system. However, since a residual speech signal is included in the input signal of a noise reconstruction filter (NRF) used for reconstructing the background noise, the long time average value of error signal for estimating the background noise is needed not to estimate the speech signal. Therefore, the ability of tracking the non-stationary noise is decreased. In order to solve this problem, we propose the noise reconstruction system with adaptive line enhancer (ALE). Since ALE works to obtain the signal occupied by noise components, the input signal of the NRF includes only a few speech components. Therefore, we can give the high tracking ability to NRF.

In this paper, we propose a step size control method capable of quickly canceling acoustic echo even when double talk continues from the echo path change. This method controls the step size by substituting the norm of the difference vector between the coefficient vectors of a main adaptive filter (Main-ADF) and a sub-adaptive filter (Sub-ADF) for the estimation error provided by the former. Actually, the number of taps of Sub-ADF is limited to a quarter of that of Main-ADF, and the larger step size than that applied to Main-ADF is given to Sub-ADF; accordingly the norm of the difference vector quickly approximates to the estimation error. The estimation speed can be improved by utilizing the norm of the difference vector for the step size control in Main-ADF. We show using speech signals that in single talk the proposed method can provide almost the same estimation speed as the method whose step size is fixed at the optimum one and verify that even in double talk the estimation error, quickly decreases.

This paper proposes a new method to reduce sinusoidal noise components whose frequencies are known. The new method is based on the simultaneous equations technique. The technique does not require the secondary path filter: thereby the automatic recovering of the noise reduction effect deteriorated by secondary path changes becomes possible. This paper also presents computer simulation results to examine the performance of the new method.

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 simultaneous equations method is one of active noise control algorithms without estimating an error path. This algorithm requires identification of a transfer function from a reference microphone to an error microphone containing the effect of a noise control filter. It is achieved by system identification of an auxiliary filter. However, the introduction of the auxiliary filter requires more number of samples to obtain the noise control filter and brings a requirement of some undesirable assumption in the multiple channel case. In this paper, a new simultaneous equations method without the identification of the auxiliary filter is proposed. By storing a small number of input signals and error signals, we avoid this identification. Therefore, we can reduce the number of samples to obtain the noise control filters and can avoid the undesirable assumption. From simulation examples, it is verified that the merits of the ordinary method is also retained in the proposed method.

A technique that uses a linear prediction error filter (LPEF) and an adaptive digital filter (ADF) to achieve noise reduction in a speech degraded by additive background noise is proposed. It is known that the coefficients of the LPEF converge such that the prediction error signal becomes white. Since a voiced speech can be represented as the stationary periodic signal over a short interval of time, most of voiced speech cannot be included in the prediction error signal of the LPEF. On the other hand, when the input signal of the LPEF is a background noise, the prediction error signal becomes white. Assuming that the background noise is represented as generate by exciting a linear system with a white noise, then we can reconstruct the background noise from the prediction error signal by estimating the transfer function of noise generation system. This estimation is performed by the ADF which is used as system identification. Noise reduction is achieved by subtracting the noise reconstructed by the ADF from the speech degraded by additive background noise.

For multiple-channel active noise control (ANC) systems, distributed systems consisting of more than one controller are useful. In this paper, we propose a performance improvement algorithm for the distributed multiple-channel ANC system based on the simultaneous equations method. In the proposed algorithm, no estimation of error paths is required. This algorithm can provide good performance in canceling primary noises with auto-/cross-correlations and achieve stable noise reduction under a change of the error paths.

In this paper, we propose a method capable of shortening the distance from a noise detection microphone to a loudspeaker, which is one of important issues in the field of active noise control (ANC). In the ANC system, the secondary noise provided by the loudspeaker is required arriving at an error microphone simultaneously with the primary noise to be cancelled. However, the reverberation involved in the secondary path from the loudspeaker to the error microphone increases the secondary noise components arriving later than the primary noise. The late components are not only invalid for canceling the primary noise but also impede the cancellation. To reduce the late components, the distance between the noise detection microphone and the loud speaker is generally extended. The proposed method differently reduces the late components by forming the noise control filter, which produces the secondary noise, with the cascade connection of a non-recursive and a recursive filters. The distance can be thus shortened. On the other hand, the recursive filter is required to work stably. The proposed method guarantees the stable work by forming the recursive filter with the lattice filter whose coefficients are restricted to less than unity.