For reduction of computational complexity in the IA algorithm, the thinned-out IA algorithm in which only one step size is updated every iteration is proposed and is complementarily switched with the HA algorithm according to the convergence. The switching is determined by using the gradient of the error signal power. These are investigated through the computer simulations.

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.

In this paper we present an analysis based on the indirect Lyapunov criteria, that is used to study the convergence of an infinite impulse response (IIR) adaptive digital filter (ADF) based on estimation of the allpass system. The analysis is then extended to investigate the necessity of directly estimating the transfer level of the unknown system. We consider two cases of modeling the ADF. In the first system, the allpass section of the ADF estimates only the real poles of the unknown system while in the second system, both real and complex poles the allpass section are estimated. From the analysis and computer simulation, we realize that the poles of the ADF converge selectively to the poles of the unknown system, depending on the sign of the step size of adaptation. Using these results we proposed a new method to control the convergence of the poles the IIR ADF based on estimation of the allpass system.

As the nonlinear adaptive filter, the neural filter is utilized to process the nonlinear signal and/or system. However, the neural filter requires large number of iterations for convergence. This letter presents a new structure of the multi-layer neural filter where the orthonormal transform is introduced into all inter-layers to accelerate the convergence speed. The proposed structure is called the transform domain neural filter (TDNF) for convenience. The weights are basically updated by the Back-Propagation (BP) algorithm but it must be modified since the error back-propagates through the orthogonal transform. Moreover, the variable step size which is normalized by the transformed signal power is introduced into the BP algorithm to realize the orthonormal transform. Through the computer simulation, it is confirmed that the introduction of the orthonormal transform is effective for speedup of convergence in the neural filter.

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.

This paper presents a multi-matcher on-line signature verification system which fuses the verification scores in pen-position parameter and pen-movement angle one at total decision. Features of pen-position and pen-movement angle are extracted by the sub-band decomposition using the Discrete Wavelet Transform (DWT). In the pen-position, high frequency sub-band signals are considered as individual features to enhance the difference between a genuine signature and its forgery. On the other hand, low frequency sub-band signals are utilized as features for suppressing the intra-class variation in the pen-movement angle. Verification is achieved by the adaptive signal processing using the extracted features. Verification scores in the pen-position and the pen-movement angle are integrated by using a weighted sum rule to make total decision. Experimental results show that the fusion of pen-position and pen-movement angle can improve verification performance.

This paper first presents the performance analysis of the NACF algorithm. The results show the possibility of the degradation in the convergence speed. To improve the convergence speed, the bias term is introduced into the NACF algorithm and its efficiency is investigated through the computer simulations.

In on-line signature verification, complexity of signature shape can influence the value of the optimal threshold for individual signatures. Writer-dependent threshold selection has been proposed but it requires forgery data. It is not easy to collect such forgery data in practical applications. Therefore, some threshold equalization method using only genuine data is needed. In this letter, we propose three different threshold equalization methods based on the complexity of signature. Their effectiveness is confirmed in experiments using a multi-matcher DWT on-line signature verification system.

Recently, in the modem, the spread spectrum communication system and the software radio, Digital Signal Processor type Squaring Loop (DSP-squaring-loop) is employed in the demodulation of Binary Phase Shift Keying (BPSK) signal. The DSP-squaring-loop extracts the carrier signal that is used for the coherent detection. However, in case the Signal to Noise Ratio (SNR) is low, the DSP-Phase Locked Loop (DSP-PLL) can not pull in the frequency offset and the phase offset. In this paper, we propose a DSP-squaring-loop that is robust against noise and which uses the adaptive notch filter type frequency estimator and the adaptive Band Pass Filter (BPF). The proposed method can extract the carrier signal in the low SNR environment. The effectiveness of the proposed method is confirmed by the computer simulation results.

In this paper, we propose a new structure of the frequency domain adaptive filter (FDAF). The proposed structure is based on the modified DFT pair which consists of the FIR filters, so that un-delayed output signal can be obtained with stable convergence and without accumulated error which are problems for the conventional FDAFs. The convergence performance of the proposed FDAF is examined through the computer simulations in the adaptive line enhancer (ALE) comparing with the conventional FDAF and the DCT domain adaptive filter. Furthermore, in order to improve the error performance of the FDAF, we propose a composite algorithm which consists of the normalized step size algorithm for fast convergence and the variable step size one for small estimation error. The advantage of the proposed algorithm is also confirmed through simulations in the ALE. Finally, we propose a reduction method of the computational complexity of the proposed FDAF. The proposed method is to utilize a part of the FFT flow-graph, so that the computational complexity is reduced to O(N log N).

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.

Speech enhancement has been proposed to reduce the impulsive noise whose frequency characteristic is wideband. On the other hand, it is challenging to reduce the ringing sound, which is narrowband in impulsive noise. Therefore, we propose the modeling of the ringing sound and its estimation by a linear predictor (LP). However, it is difficult to estimate the ringing sound only in noisy speech due to the auto-correlation property of speech. The proposed system adopts the 4th order moment-based adaptive algorithm by noticing the difference between the 4th order statistics of speech and impulsive noise. The brief analysis and simulation results show that the proposed system has the potential to reduce ringing sound while keeping the quality of enhanced speech.

A noise reduction technique that uses the linear prediction to remove noise components in speech signals has been proposed previously. The noise reduction works well for additive white noise signals, because the coefficients of the linear predictor converge such that the prediction error becomes white. In this method, the linear predictor is updated by a gradient-based algorithm with a fixed step-size. However, the optimal value of the step-size changes with the values of the prediction coefficients. In this paper, we propose a noise reduction system using the linear predictor with a variable step-size. The optimal value of the step-size depends also on the variance of the white noise, however the variance is unknown. We therefore introduce a speech/non-speech detector, and estimate the variance in non-speech segments where the observed signal includes only noise components. The simulation results show that the noise reduction capability of the proposed system is better than that of the conventional one with a fixed step-size.

In order to reduce the computational complexity of the DWT domain on-line signature verification, the authors propose to utilize the pen-movement vector as an input parameter. Experimental results indicate that the verification rate obtained using the pen-movement vector parameter is equivalent to that obtained by the conventional method, although the computational complexity of the proposed method is approximately half that of the conventional method.

We propose an auxiliary-noise power-scheduling method for a pre-inverse active noise control (PIANC) system. Conventional methods cannot reduce the power of auxiliary-noise due to the use of the filtered-x least mean square (FxLMS) algorithm. We developed our power-scheduling method for a PIANC system to solve this problem. Since a PIANC system uses a delayed input signal for a control filter, the proposed method delivers stability even if the acoustic path fluctuates. The proposed method also controls the gain of the auxiliary-noise based on the secondary-path-modeling state. The proposed method determines this state by the variation in the power of the secondary-path-modeling-error signal. Thus, the proposed method changes the power-scheduling of the auxiliary-noise. When the adaptive algorithm does not sufficiently converge, the proposed method injects auxiliary-noise. However, auxiliary-noise stops when the adaptive algorithm sufficiently converges. Therefore, the proposed method improves noise reduction performance.

The speech noise reduction system based on the frequency domain adaptive line enhancer using a windowed modified DFT (MDFT) pair is presented. The adaptive line enhancer (ALE) is effective for extracting sinusoidal signals blurred by a broadband noise. In addition, it utilizes only one microphone. Therefore, it is suitable for the realization of speech noise reduction in portable electronic devices. In the ALE, an input signal is generated by delaying a desired signal using the decorrelation parameter, which makes the noise in the input signal decorrelated with that in the desired one. In the present paper, we propose to set decorrelation parameters in the frequency domain and adjust them to optimal values according to the relationship between speech and noise. Such frequency domain decorrelation parameters enable the reduction of the computational complexity of the proposed system. Also, we introduce the window function into MDFT for suppressing spectral leakage. The performance of the proposed noise reduction system is examined through computer simulations.

In this paper, we propose a speedup method of frequency switching time in the phase locked loop (PLL) frequency synthesizer using the target frequency detector (TFD). The TFD detects the time Ta for any channels where the output of the PLL frequency synthesizer reaches the target frequency for the first time. At Ta, the programmable divider, the reference divider and the phase comparator are reset, and the phase of the PLL frequency synthesizer is initialized and the phase synchronization is achieved. In the proposed method, since the ringing in the transient state does not occur, the output of the PLL frequency synthesizer converges to the target frequency at Ta and the frequency switching time is speeded up. The effectiveness of the proposed method will be confirmed by experimental results.

The lock-up time of a PLL frequency synthesizer mainly depends on the total loop gain. Since the gain of the conventional phase detector is constant, it is difficult to improve the lock-up time by the phase detector. In this paper, we reconsider the operation of the phase detector and propose the PLL frequency synthesizer with multi-phase detector in which the gain of phase detector is increased by using four stage phase detectors and charge pumps. Then, a higher speed lock-up time and good spurious characteristics can be achieved.

Newton based adaptive algorithms are among the algorithms which are known to exhibit a higher convergence speed in comparison to the least mean square (LMS) algorithms. In this paper we propose a simplified Newton based adaptive algorithm for an adaptive infinite impulse response (IIR) filter implemented using cascades of second order allpass filters and a finite impulse response (FIR) filter. The proposed Newton based algorithm avoids the complexity that may arise in the direct differentiation of the mean square error. The analysis and simulation results presented for the algorithm, show that the property of convergence of the poles of the IIR ADF to those of the unknown system will be maintained for both white and colored input signal. Computer simulation results confirm an increase in convergence speed in comparison to the LMS algorithm.

In this paper we propose a parallel composition based adaptive notch filter for eliminating sinusoidal signals whose frequencies are unknown. The proposed filter which is implemented using second order all-pass filter and a band-pass filter can achieve high convergence speed by using the output of an additional band-pass filter to update the coefficients of the notch filter. The high convergence speed of the proposed notch filter is obtained by reducing an effect that an updating term of coefficient for adaptation of a notch filter significantly increases when the notch frequency approaches the sinusoidal frequency. In this paper, we analyze such effect obtained by the additional band-pass filter. We also present an analysis of a convergence performance of cascaded system of the proposed notch filter for eliminating multiple sinusoids. Simulation results have shown the effectiveness of the proposed adaptive notch filter.