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 an adaptive algorithm for identifying unknown systems containing nonlinear amplitude characteristics. Usually, the nonlinearity is so small as to be negligible. However, in low cost systems, such as acoustic echo canceller using a small loudspeaker, the nonlinearity deteriorates the performance of the identification. Several methods preventing the deterioration, polynomial or Volterra series approximations, have been hence proposed and studied. However, the conventional methods require high processing cost. In this paper, we propose a method approximating the nonlinear characteristics with a piecewise linear curve and show using computer simulations that the performance can be extremely improved. The proposed method can also reduce the processing cost to only about twice that of the linear adaptive filter system.

We propose a new robust frequency domain acoustic echo cancellation filter that employs a normalized residual echo enhancement. By interpreting the conventional robust step-size control approaches as a statistical-model-based residual echo enhancement problem, the optimal step-size introduced in the most of conventional approaches is regarded as optimal only on the assumption that both the residual echo and the outlier in the error output signal are described by Gaussian distributions. However, the Gaussian-Gaussian mixture assumption does not always hold well, especially when both the residual echo and the outlier are speech signals (known as a double-talk situation). The proposed filtering scheme is based on the Gaussian-Laplacian mixture assumption for the signals normalized by the reference input signal amplitude. By comparing the performances of the proposed and conventional approaches through the simulations, we show that the Gaussian-Laplacian mixture assumption for the normalized signals can provide a better control scheme for the acoustic echo cancellation.

We propose a gradient-limited affine projection algorithm (GL-APA), which can achieve fast and double-talk-robust convergence in acoustic echo cancellation. GL-APA is derived from the M-estimation-based nonlinear cost function extended for evaluating multiple error signals dealt with in the affine projection algorithm (APA). By considering the nonlinearity of the gradient, we carefully formulate an update equation consistent with multiple input-output relationships, which the conventional APA inherently satisfies to achieve fast convergence. We also newly introduce a scaling rule for the nonlinearity, so we can easily implement GL-APA by using a predetermined primary function as a basis of scaling with any projection order. This guarantees a linkage between GL-APA and the gradient-limited normalized least-mean-squares algorithm (GL-NLMS), which is a conventional algorithm that corresponds to the GL-APA of the first order. The performance of GL-APA is demonstrated with simulation results.

This paper proposes a new echo suppressor based on spectral correlation between the residual echo and the echo replica in an ordinary echo canceller. First, it is revealed by experiments that there is a significant correlation between the spectral amplitudes of the residual echo and the echo replica, and a new model for nonlinear-echo suppression based on the correlation is derived. Next, a new echo suppressor controlling the gain in each frequency bin to suppress the residual echo based on the new model is developed. Simulation results with speech data recorded by a hands-free cellphone show that the proposed echo suppressor reduces the residual echo to an almost inaudible level.

Recently, automatic speech recognition in a car has practical uses for applications like car-navigation and hands-free telephone dialers. For noise robustness, the current successes are based on the assumption that there is only a stationary cruising noise. Therefore, the recognition rate is greatly reduced when there is music or news coming from a radio or a CD player in the car. Since reference signals are available from such in-vehicle units, there is great hope that echo cancellers can eliminate the echo component in the observed noisy signals. However, previous research reported that the performance of an echo canceller is degraded in very noisy conditions. This implies it is desirable to combine the processes of echo cancellation and noise reduction. In this paper, we propose a system that uses echo cancellation and spectral subtraction simultaneously. A stationary noise component for spectral subtraction is estimated through the adaptation of an echo canceller. In our experiments, this system significantly reduced the errors in automatic speech recognition compared with the conventional combination of echo cancellation and spectral subtraction.

This paper proposes an alternative learning algorithm for a stereophonic acoustic echo canceller without pre-processing which can identify the correct echo-paths. By dividing the filter coefficients into the former/latter parts and updating them alternatively, conditions both for unique solution and for perfect echo cancellation are satisfied. The learning for each part is switched from one part to the other when that part converges. Convergence analysis clarifies the condition for correct echo-path identification. For fast and stable convergence, a convergence detection and an adaptive step-size are introduced. The modification amount of the filter coefficients determines the convergence state and the step-size. Computer simulations show 10 dB smaller filter coefficient error than those of the conventional algorithms without pre-processing.

We investigated the dependence of the desired echo return loss on frequency for various hands-free telecommunication conditions by subjective assessment. The desired echo return loss as a function of frequency (DERLf) is an important factor in the design and performance evaluation of a subband echo canceller, and it is a measure of what is considered an acceptable echo caused by electrical loss in the transmission line. The DERLf during single-talk was obtained as attenuated band-limited echo levels that subjects did not find objectionable when listening to the near-end speech and its band-limited echo under various hands-free telecommunication conditions. When we investigated the DERLf during double-talk, subjects also heard the speech in the far-end room from a loudspeaker. The echo was limited to a 250-Hz bandwidth assuming the use of a subband echo canceller. The test results showed that: (1) when the transmission delay was short (30 ms), the echo component around 2 to 3 kHz was the most objectionable to listeners; (2) as the transmission delay rose to 300 ms, the echo component around 1 kHz became the most objectionable; (3) when the room reverberation time was relatively long (about 500 ms), the echo component around 1 kHz was the most objectionable, even if the transmission delay was short; and (4) the DERLf during double-talk was about 5 to 10 dB lower than that during single-talk. Use of these DERLf values will enable the design of more efficient subband echo cancellers.

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.

An Acoustic echo canceller has problems adaptating under noisy or double-talk conditions. The adaptation process requires a precise identification of the temporarily changed room impulse response. To do this, both minimizing the step size parameter of the Least Mean Square (LMS) method to be as small as possible and giving up on updating the adaptive filter coefficients have been considered. This paper describes an adaptive cross-spectral technique that is robust to adaptive filtering under noisy or double-talk conditions and for colored signals such a speech signal. The cross-spectral technique was originally developed to measure the impulse response in a linear system. Here we apply in the adaptive cross-spectral technique to solve the acoustic echo cancelling problem. This cross-spectral technique takes the ensemble average of the cross spectrum between input and error signals and the averaged cross spectrum is divided by the averaged power spectrum of the input signal to update the filter coefficients. We have confirmed that the echo signal is suppressed by about 15 dB even under double-talk conditions. We also explain that this method has a systematic error due to using a short time block for estimating the room impulse response. Then we investigate overlapping every last half block by the following first half block in order to reduce the effect of the systematic error. Finally, we compare our method with the Frequency-domain Block LMS (FBLMS) method because both methods are implemented in the frequency domain using a short time block.

In this paper, we propose an adaptive lattice-transversal joint (LTJ) filter structure that is quite suitable for the practical implementation of the acoustic echo canceller. The structure maintains fast convergence of the lattice structure and low computational complexity of the transversal structure simultaneously. It is particularly more efficient in memory usage than any other existing fast-convergent algorithm for the acoustic echo cancellation.

This work is targeted to understand the operating principle of the feedback type echo canceller for use in an FM broadcasting receiver and to study its compensating features and the effects of the practical operating environment on its performance. The effects of the tap interval and the compensation performance in the presence of an echo with excess delay 0 - 15 µs are examined. The results show that the tap interval should be selected according to the observable bandwidth of the channel transfer function and the performance of a feedback type echo canceller has a wavelike curve with respect to the excess delay of the echo. To improve the performance of the feedback type echo canceller, an adaptive echo canceller operating with CM algorithm is proposed and examined with computer simulation. The results show that the compensation performance is improved.

This paper proposes a modified normalized LMS algorithm based on a long-term average of the reference input signal power. The reference input signal power for normalization is estimated by using two leaky integrators with a short and a long time constants. Computer simulation results compare the performance of the proposed algorithm with some previosuly proposed adaptive-step algorithms. The proposed algorithm converges faster than the conventional adaptive-step algorithms. Almost 30dB of the ERLE (Echo Return Loss Enhancement), which is comparable to the conventional algorithms, is achieved in noisy environments.

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.

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.

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.

An acoustic echo canceller that also cancels room noise is proposed. This system has an additive (noise reference) input port, and a noise canceller (NC) precedes the echo canceller (EC) in a cascade configuration. The adaptation control problem for the cascaded echo and noise canceller is solved by controlling the adaptation process to match the occurrence of intermittent speech/echo; the room noise is a stationary signal. A simulation shows that adaptation using the NLMS algorithm is very effective for the echo and noise cancellation. Sub-band cancelling techniques are utilized. Noise cancellation is realized with a lower band EC. Hardware is implemented and its performance evaluated through experiments under a real acoustic field. The combination of the EC with NC maintains excellent performance at all echo to room noise power ratios. It is shown that the proposed canceller overcomes the disadvantages traditionally associated with ECs and NSc.

This paper proposes a new adaptive algorithm of the FIR type digital filter for an acoustic echo canceller and similar application fields. Unlike an echo canceller for line, an acoustic echo canceller requires a large number of taps, and it must work appropriately while it is driven by colored input signal. By controlling the filter tap length and updating filter coefficients multiple times during a single sampling interval, the proposed algorithm improves the convergence characteristics of adaptation even if colored input signal is introduced. This algorithm is maned VT-LMS after variable tap length LMS. The results of simulation show the effectiveness of the proposed algorithm not only for white noise but also for colored input signal such as speech. The VT-LMS algorithm has better convergence characteristice with very little extra computational load compared to the conventional algorithm.

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 paper proposes a new adaptive algorithm for acoustic echo cancellers with four times the convergence speed for a speech input, at almost the same computational load, of the normalized LMS (NLMS). This algorithm reflects both the statistics of the variation of a room impulse response and the whitening of the received input signal. This algorithm, called the ESP (exponentially weighted step-size projection) algorithm, uses a different step size for each coefficient of an adaptive transversal filter. These step sizes are time-invariant and weighted proportional to the expected variation of a room impulse response. As a result, the algorithm adjusts coefficients with large errors in large steps, and coefficients with small errors in small steps. The algorithm is based on the fact that the expected variation of a room impulse response becomes progressively smaller along the series by the same exponential ratio as the impulse response energy decay. This algorithm also reflects the whitening of the received input signal, i.e., it removes the correlation between consecutive received input vectors. This process is effective for speech, which has a highly non-white spectrum. A geometric interpretation of the proposed algorithm is derived and the convergence condition is proved. A fast profection algorithm is introduced to reduce the computational complexity and modified for a practical multiple DSP structure so that it requires almost the same computational load, 2L multiply-add operations, as the conventional NLMS. The algorithm is implemented in an acoustic echo canceller constructed with multiple DSP chips, and its fast convergence is demonstrated.