For the nonlinear acoustic echo cancellation, we present an algorithm to estimate the threshold of the clipping effect and the room impulse response vector by suppressing their time-varying cost function. A common way to suppress the time-varying cost function of a pair of parameters is to alternatingly minimize the function with respect to each parameter while keeping the other fixed, which we refer to as adaptive alternating minimization. However, since the cost function for the threshold is nonconvex, the conventional methods approximate the exact minimizations by gradient descent updates, which causes serious degradation of the estimation accuracy in some occasions. In this paper, by exploring the fact that the cost function for the threshold becomes piecewise quadratic, we propose to exactly minimize the cost function for the threshold in a closed form while suppressing the cost function for the impulse response vector in an online manner, which we call exact-online adaptive alternating minimization. The proposed method is expected to approximate more efficiently the adaptive alternating minimization strategy than the conventional methods. Numerical experiments demonstrate the efficacy of the proposed method.

Determining an effective way to reduce computation complexity is an essential task for adaptive echo cancellation applications. Recently, a family of partial update (PU) adaptive algorithms has been proposed to effectively reduce computational complexity. However, because a PU algorithm updates only a portion of the weights of the adaptive filters, the rate of convergence is reduced. To address this issue, this paper proposes an enhanced switching-based variable step-size (ES-VSS) approach to the M-max PU least mean square (LMS) algorithm. The step-size is determined by the correlation between the error signals and their noise-free versions. Noise-free error signals are approximated according to the level of convergence achieved during the adaptation process. The approximation of the noise-free error signals switches among four modes, such that the resulting step-size is as close to its optimal value as possible. Simulation results show that when only a half of all taps are updated in a single iteration, the proposed method significantly enhances the convergence rate of the M-max PU LMS algorithm.

In this paper, we propose a use of the group sparsity in adaptive learning of second-order Volterra filters for the nonlinear acoustic echo cancellation problem. The group sparsity indicates sparsity across the groups, i.e., a vector is separated into some groups, and most of groups only contain approximately zero-valued entries. First, we provide a theoretical evidence that the second-order Volterra systems tend to have the group sparsity under natural assumptions. Next, we propose an algorithm by applying the adaptive proximal forward-backward splitting method to a carefully designed cost function to exploit the group sparsity effectively. The designed cost function is the sum of the weighted group l1 norm which promotes the group sparsity and a weighted sum of squared distances to data-fidelity sets used in adaptive filtering algorithms. Finally, Numerical examples show that the proposed method outperforms a sparsity-aware algorithm in both the system-mismatch and the echo return loss enhancement.

Regularization plays a fundamental role in adaptive filtering. There are, very likely, many different ways to regularize an adaptive filter. In this letter, we propose one possible way to do it based on a condition that makes intuitively sense. From this condition, we show how to regularize the recursive least-squares (RLS) algorithm.

This paper presents a single-chip speech dialogue module and its evaluation on a personal robot. This module is implemented on an application processor that was developed primarily for mobile phones to provide a compact size, low power-consumption, and low cost. It performs speech recognition with preprocessing functions such as direction-of-arrival (DOA) estimation, noise cancellation, beamforming with an array of microphones, and echo cancellation. Text-to-speech (TTS) conversion is also equipped with. Evaluation results obtained on a new personal robot, PaPeRo-mini, which is a scale-down version of PaPeRo, demonstrate an 85% correct rate in DOA estimation, and as much as 54% and 30% higher speech recognition rates in noisy environments and during robot utterances, respectively. These results are shown to be comparable to those obtained by PaPeRo.

In this Letter, a residual acoustic echo suppression method is proposed to enhance the speech quality of hands-free communication in an automobile environment. The echo signal is normally a human voice with harmonic characteristics in a hands-free communication environment. The proposed algorithm estimates the residual echo signal by emphasizing its harmonic components. The estimated residual echo is used to obtain the signal-to-interference ratio (SIR) information at the acoustic echo canceller output. Then, the SIR based Wiener post-filter is constructed to reduce both the residual echo and noise. The experimental results confirm that the proposed algorithm is superior to the conventional residual echo suppression algorithm in terms of the echo return loss enhancement (ERLE) and the segmental signal-to-noise ratio (SEGSNR).

This letter proposes a windowing frequency domain adaptive algorithm, which reuses the filtering error to apply window function in the filter updating symmetrically. By using a proper window function to reduce the negative influence of the spectral leakage, the proposed algorithm can significantly improve the performance of the acoustic echo cancellation for speech signals.

A simple and novel residual acoustic echo cancellation method that employs binary masking is proposed to enhance the speech quality of hands-free communication in an automobile environment. In general, the W-disjoint orthogonality assumption is used for blind source separation using multi-microphones. However, in this Letter, it is utilized to mask the residual echo component in the time-frequency domain using a single microphone. The experimental results confirm the effectiveness of the proposed method in terms of the echo return loss enhancement and speech enhancement.

To attain good performance in an acoustic echo cancellation system, it is important to have a variable step size (VSS) algorithm as part of an adaptive filter. In this paper, we are concerned with the development of a VSS algorithm for a recently proposed subband affine projection (SAP) adaptive filter. Two popular VSS algorithms in the literature are the methods of delayed coefficients (DC) and variable regularization (VR). However, the merits and demerits of them are mutually exclusive. We propose a VSS algorithm that is a hybrid of both methods and combines their advantages. An extensive study of the new algorithm in different scenarios like the presence double-talk (DT) during the transient phase of the adaptive filter, DT during steady state, and varying DT power is conducted and reasoning is given to support the observed behavior. The importance of the method of VR as part of a VSS algorithm is emphasized.

This Letter proposes an optimal gain filter for the perceptual acoustic echo suppressor. We designed an optimally-modified log-spectral amplitude estimation algorithm for the gain filter in order to achieve robust suppression of echo and noise. A new parameter including information about interferences (echo and noise) of single-talk duration is statistically analyzed, and then the speech absence probability and the a posteriori SNR are judiciously estimated to determine the optimal solution. The experiments show that the proposed gain filter attains a significantly improved reduction of echo and noise with less speech distortion.

A residual acoustic echo cancellation method that employs the masking property is proposed to enhance the speech quality of hands-free communication devices in an automobile environment. The conventional masking property is employed for speech enhancement using the masking threshold of the desired clean speech signal. In this Letter, either the near-end speech or residual noise is selected as the desired signal according to the double-talk detector. Then, the residual echo signal is masked by the desired signal (masker). Experiments confirm the effectiveness of the proposed method by deriving the echo return loss enhancement and by examining speech waveforms and spectrograms.

In the acoustic echo canceller (AEC), the step-size parameter of the adaptive filter must be varied according to the situation if double talk occurs and/or the echo path changes. We propose an AEC that uses a sub-adaptive filter. The proposed AEC can control the step-size parameter according to the situation. Moreover, it offers superior convergence compared to the conventional AEC even when the double talk and the echo path change occur simultaneously. Simulations demonstrate that the proposed AEC can achieve higher ERLE and faster convergence than the conventional AEC. The computational complexity of the proposed AEC can be reduced by reducing the number of taps of the sub-adaptive filter.

This letter proposes a novel nonlinear distortion for the unique identification of receiving room impulses in stereo acoustic echo cancellation when applying the frequency-domain adaptive filtering technique. This nonlinear distortion is effective in reducing the coherence between the two incoming audio channels and its influence on audio quality is inaudible.

An adaptive interpolated FIR (IFIR) echo canceller was recently proposed for xDSL applications. This canceller consists of an FIR filter, an IFIR filter, and a tap-weight overlapping and nulling scheme. The FIR filter is used to cancel the short and rapidly changing head echo while the IFIR filter is used to cancel the long and slowly decaying tail echo. This echo canceller, which inherits the stable characteristics of the conventional FIR filter, requires low computational complexity. It is well known that the interpolation filter for an IFIR filter has great influence on the interpolated result. In this paper, a least-squares method is proposed to obtain optimal interpolation filters such that the performance of the IFIR echo canceller can be further improved. Simulations with a wide variety of loop topologies show that the optimal IFIR echo canceller can effectively cancel the echo up to 73.0 dB (for an SHDSL system). About 57% complexity reduction can be achieved compared to a conventional FIR filter.

We consider a new post-filtering algorithm for residual acoustic echo cancellation in hands-free application. The new post-filtering algorithm is composed of AR analysis, pitch prediction, and noise reduction algorithm. The residual acoustic echo is whitened via AR analysis and pitch prediction during no near-end talker period and then is cancelled by noise reduction algorithm. By removing speech characteristics of the residual acoustic echo, noise reduction algorithm reduces the power of the residual acoustic echo as well as the ambient noise. For the hands-free application in the moving car, the proposed system attenuated the interferences more than 15 dB at a constant speed of 80 km/h.

The adaptive cross-spectral (ACS) technique recently introduced by Okuno et al. provides an attractive solution to acoustic echo cancellation (AEC) as it does not require double-talk (DT) detection. In this paper, we first introduce a generalized ACS (GACS) technique where a step-size parameter is used to control the magnitude of the incremental correction applied to the coefficient vector of the adaptive filter. Based on the study of the effects of the step-size on the GACS convergence behaviour, a new variable step-size ACS (VSS-ACS) algorithm is proposed, where the value of the step-size is commanded dynamically by a special finite state machine. Furthermore, the proposed algorithm has a new adaptation scheme to improve the initial convergence rate when the network connection is created. Experimental results show that the new VSS-ACS algorithm outperforms the original ACS in terms of a higher acoustic echo attenuation during DT periods and faster convergence rate.

In the multipath mobile channel, the received signal suffers from both the fluctuation in the received field intensity caused by fading and waveform distortion caused by the echo. Diversity reception using multiple spaced antennas is an effective method to compensate for fading, while echo cancellation with an adaptive array is good at compensating for waveform distortion. In this paper, an adaptive switching echo cancellation/diversity reception method to compensate for both waveform distortion and fading is proposed. The proposed switching reception monitors the impacts of channel conditions on received signal and then one of an echo canceller and a diversity receiver is selected accordingly to compensate the channel. The compensation performance of the proposed switching reception in terms of both average DUR (Desired to Undesired signal Ratio) and the probability of DUR below a threshold value is investigated with computer simulation. The results show that the adaptive switching echo cancellation/diversity reception has realized the advantages of both adaptive echo cancellation and diversity reception.

A new variable step size Least Mean Square (LMS) FIR adaptive filter algorithm (VSS-CC) is proposed. In the VSS-CC algorithm the step size adjustment (α) is controlled by using the correlation between the output error (e(n)) and the adaptive filter output ((n)). At small times, e(n) and (n) are correlated which will cause a large α providing faster tracking. When the algorithm converges, the correlation will result in a small size α to yield smaller misadjustments. Computer simulations show that the proposed VSS-CC algori thm achieves a better Echo Return Loss Enhancemen (ERLE) than a conventional NLMS Algorithm. The VSS-CC algorithm was also compared with another variable step algorithm, achieving the VSS-CC a better ERLE when the additive noise is incremented.

This letter proposes a time varying step size normalized LMS (TVS-NLMS) algorithm for adaptive echo canceler structures. Proposed algorithm reduces distortion during double talk, without increasing the computational cost nor decreasing the convergence rate of the normalized LMS algorithm significantly. Simulation results using white noise and actual speech signals confirm the desirable features of the proposed scheme.

This paper proposes a new subband adaptive filtering algorithm for adaptive FIR filters. The number of taps for each subband filter is adaptively controlled based on a sum of the absolute coefficients or the coefficient power in conjunction with the subband signal power. Keeping the total number of taps constant, redundant taps are redistributed to subbands where the number of taps is insufficient. Simulation results with a white signal show that the number of taps in each subband approaches an optimum as each subband filter converges. For a colored signal, tap assignment by the new algorithm is as stable as for a white signal.