Nonlinear System Identification Using a Subband Adaptive Volterra Filter

In hands-free telephone or video conference application, there exists an acoustic feedback coupling between the loudspeaker and microphone in an enclosed environment, which creates the acoustic echo. FIR filters are commonly used in acoustic echo cancellers because of their simple structure. However, in this paper, the Kautz and Laguerre filter structures are shown to be more efficient echo cancellers than the FIR filters, because they can describe accurately the acoustic echo system with fewer parameters. These filters are built from their respective orthonormal Kautz and Laguerre basis functions. The proposal is motivated by some theoretical and numerical results that the time-varying acoustic echo path is basically due to its time-varying zeros and not its time-invariant acoustical poles. Therefore, the poles of the Kautz and the Laguerre filters are estimated, and can be kept fixed or updated occasionally if required. The poles are estimated by a batch Gauss-Newton algorithm. Then, the coefficients of the Kautz and Laguerre filters can be estimated by most recursive algorithms that are suitable for linear regression models, e.g., the normalized LMS algorithm. Generally, it is shown that the proposed Kautz and Laguerre filters, as the filter structures in an acoustic echo canceller, have better convergence and tracking properties than the FIR and IIR filters.

An acoustic echo canceller (AEC) is often used to eliminate acoustic echoes generated in a hands-free communication system. AEC approximates the echo path using an adaptive filter and eliminates the acoustic echo by subtracting the echo replica generated using the filter from the observed signal. However, conventional adaptive algorithms for updating filters fail to estimate during double talk periods when both acoustic echo and near-end speech are observed. In recent years, it was shown that blind source separation (BSS) can be used for echo cancellation during double talk period. However, the convergence speed is slower than the convergence speed of the conventional method. In our previous research we proposed combining conventional AEC and BSS, where BSS was used as preprocessor of adaptive filter to perform echo cancellation. Since there is room for improvement in the echo cancellation performance in previous research, in this paper we propose a new approach using BSS alternative output as AEC input. Simulation results show that the proposed system is effective for echo path estimation when echo path fluctuation occurs during double talk period.

In this paper, a single channel Kalman filtering in the spectral domain is proposed for acoustic echo and noise cancellation (AENC). An augmented extended Kalman filter is used in this work which is suitable for processing general complex-valued nonlinear and nonstationary signals. The near-end speech and background noise are modelled to make the filter robust against double-talk and against noise. In this work, the approach for echo cancellation does not depend on double-talk detector (DTD) to model the echo path, when signals other than the echoed far-end signal are present. The performance evaluations are conducted over the NOIZEUS corpus. The experimental results demonstrate that the ideal Kalman filter (with near-end speech parameters) outperforms other enhancement methods of echo cancellation in terms of subjective and objective measures. The non-ideal Kalman filter (when near-end speech parameters are obtained by an EM algorithm) also shows promising results after the ideal Kalman filter.

Sub-band filtering is employed in various system identification applications. It reduces the computational complexity and adaptation time. The paper focuses on sub-band filtering for acoustic echo cancellation. A Simulink model for 2, 4, 8 sub-bands was created and simulated. The echo is created by applying a vocal test to a first Schroeder reverberator. The performance of the full- and sub-band echo canceller is measured in terms of error signal and echo-return loss enhancement.

An acoustic echo canceler (AEC) is often employed to remove the acoustic echoes generated in hands-free communication systems. The AEC cancels the acoustic echoes by approximating the echo-path with the use of an adaptive filter and subtracting the pseudo echoes generated by the filter from the observed signal. The conventional adaptive algorithm for updating the filter, however, fails in estimation of the echo-path during double-talk when both the acoustic echo and the near-end speech are observed. Recently it has been shown that the blind source separation (BSS) can be employed to perform the echo cancelation during the double-talk; however, the convergence speed is slower than that of the conventional method. In this paper, a system is proposed that combines the conventional AEC and BSS; essentially, BSS is employed as a preprocessor for the adaptive filter performing the echo cancelation. Simulation results show that the proposed system is effective for the echo-path estimation during the double-talk as well as during variation in the echo path.

In this paper, we propose a novel realization of sub-band adaptive Volterra filter, which consists of input signal transformation block and only one adaptive Volterra filter. The proposed realization can focus on major frequency band, in which a target nonlinear system has dominant components, by changing the number of taps in each sub-band in order to simultaneously realize high computational efficiency and high identification performance. The proposed realization of sub-band adaptive Volterra filter is applied to the identification of electro-dynamic loudspeaker systems and the effectiveness is demonstrated through some simulations. Simulation results show that the proposed realization can significantly improve the estimation accuracy.

An acoustic echo cancellation system is one of the most important breakthrough in the field of adaptive systems. Today acoustic echo cancellers (AEC) are an integral part of full duplex hands-free voice communication. Conventional echo cancellers use a linear model to represent the echo path. However many consumer devices include loud-speakers and power amplifiers that generate non-linear distortions.Non-linearity occurs due to the use of low cost electronic loud speakers, microphones and poorly designed enclosures in an AEC system. Non-linearity causes vibration and harmonic distortion and also degrades the speech quality. Double talk detector (DTD) is a key component of an AEC. A DTD is used to sense when the far end signal is corrupted by the near end speech. The DTD freezes the adaptation of model filter to prevent the divergence of the adaptive filter. Various authors have proposed different algorithms for double talk detection. Some of the most popular algorithms are Geigel algorithm, cross-correlation based DTD, normalized cross correlation based DTD, variable impulse response DTD etc. In this paper several double talk detection algorithms in a non-linear platform of an AEC has been discussed.

In this contribution, we present some recent advances in the modeling of unknown nonlinearities by adaptive Volterra filters. In particular, a system identification scenario in the form of the nonlinear acoustic echo cancelation problem is considered, which is most challenging due to large kernel sizes and excitation by colored (noise) and/or nonstationary signals (speech). After reviewing the general filter structure and discussing a possibly more efficient DFT-domain realization by resorting to a multichannel representation, mainly two aspects are covered: On the one hand, convergence aspects are addressed (i) by framewise iterated updating that is shown to result in a faster adaptation for DFT-domain implementations and (ii) by combining Volterra kernels with different adaptation parameters, thus yielding an elegant method of robust step-size control. On the other hand, a modification of these combination schemes lends itself to an especially promising approach that enables an evolutionary self-configuration of the adaptive nonlinear structure while providing estimates for the optimum memory size parameters. From these developments, conclusions are drawn and some guidelines for future work are given.

In order to compensate the nonlinearly distorted echo in hands-free telephone, a memoryless power-series polynomial nonlinear acoustic echo canceller (AEC) can be used. Conventional nonlinear AEC employs a NLMS adaptive algorithm which can fail to converge in case of doubletalk or a very noisy environment. In this paper we propose a higher order correlation algorithm using a white Gaussian training signal. With comparable complexity as NLMS, it is most effective at small SNR. Computer simulations demonstrate that the proposed algorithm has a smaller steady-state echo and it is also very robust to background noise.

Acoustic echo cancellation for teleconferencing or hands-free telephony constitutes a challenging task for today's digital signal processing techniques. Compared to the well-established line echo cancellation in telephone networks, the problem takes on a considerably larger size: The impulse response of the echo path to be compensated is longer and may vary more rapidly in time. Moreover, larger bandwidths are often desirable. Therefore, acoustic echo cancellation requires algorithms that adapt far more coeffecients and converge faster than those commonly used in line echo cancelers. On the other hand, direct implementation of sufficiently advanced adaptive filtering algorithms is usually prohibited because of their computational complexity and inherent numerical difficulties. As a way out, the subband approach realizes a “divide and conquer” strategy and promises a reduction of computational complexity and favorable circumstances for fast convergence at the cost of some extra delay. After comparing several possible subband structures, the frequency subband structure is discussed in some detail. For adaptation using LMS-type algorithms, reasons for the improved convergence behavior compared to a fullband implementation are explained. Design examples are presented to illustrate the effectiveness of the method.

This paper considers an adaptive echo canceller which can be used in tasks of acoustic or electrical echo cancellation. The solution of these tasks usually requires the usage of the adaptive filters with a large number of weights. This means that a lot of computational resources of hardware or software are required for the echo canceller implementation. The usage of the Fast (computationally efficient) Affine Projection (FAP) adaptive filtering algorithm ensures the adaptive filter functional efficiency and complexity tradeoff in the echo cancellation tasks. The algorithm consists of two groups of calculations: a fast calculation of the adaptive filter error and a fast calculation of the normalizing matrix and its inversion. The second group is based on the usage of some calculations adopted from the sliding window Fast Recursive Least Squares adaptive filtering algorithms. However these calculations are potentially unstable. The given paper presents a simplified stable version of the multichannel FAP algorithm. The simplification is achieved due to the diagonalization of the normalizing matrix that ensures a simple procedure of direct calculation of this matrix and its inversion. If the echo signal is noisy (and that what always happens in our real world) then the original and the simplified FAP algorithms demonstrate about the same performance. The simulation of the two-channel acoustic echo cancellers confirms this statement.

This article explains the problem of acoustical echoes and their cancellation. It focuses on the hands‐free telephone as one of the major applications for echo cancelling devices. Beginning with a short discussion of a system formed by a loudspeaker and a microphone located within the same enclosure, the properties of speech signals and noise are described. The major part of the echo can be cancelled by an adaptive filter connected in parallel to the loudspeaker and the microphone. Residual echoes are suppressed by an additional filter within the outgoing signal path. Algorithms for the adaptation of the echo cancelling filter are described. Because of its robustness and its low computational complexity the NLMS algorithm is primarily applied. Measures to improve the speed of convergence and to avoid divergence in case of double talk or strong local noise are discussed. Echo cancellation for stereophonic systems, cancellation in subbands, and the applications of block processing techniques conclude the article.

Echo cancellation has been a major problem to be solved in VoIP, although the integrated echo cancellation module in Speex, it does not consider thread synchronization issues. The frequency domain echo cancellation algorithm MDF of speex is analyzed, and then a synchronization method of playing thread and recording thread is proposed. The results show that the acoustic echo canceller which achieved by the proposed method meet the requirements of voice communication, implementation is easier and therefore provides a reference for the VoIP voice communication and mobile communication terminal .

When we talk with hands-free in a car or noisy lobby, the performance of the echo canceller degrade because background noise added to echo caused by the distance from mouth to microphone is relatively long. It gives a reason for necessity of noise-robust and high convergence speed adaptive algorithm. And if acoustic echo canceller operated not perfectly, residual signal going through the echo canceller to far-end speaker remains residual echo, which degrade quality of talk. To solve this problem, post-processing needed to remove residual echo ones more. In this paper, we propose a new acoustic echo canceller, which has noise robust and high convergence speed adaptive estimating echo path algorithm, linked with linear predictor and Wiener filter as a post-processor. By computer simulation, it is confirmed that the proposed algorithm shows better performance from acoustic interference cancellation (AIC) viewpoint.KeywordsNoise Robust AlgorithmPost-ProcessingLinear Prediction Error FilterWiener filter

This paper deals with the problem of echo cancellation of speech signals in an acoustic environment. In this regard, generally, different adaptive filter algorithms are employed, which may lack the flexibility of controlling the convergence rate, number of iterations, range of variation of filter coefficients, and tolerance consistency. In order to overcome these problems, unlike conventional approaches, we formulate the task of echo cancellation as a coefficient optimization problem whereby we introduce the particle swarm optimization (PSO) algorithm. In this case, the PSO is designed to perform the error minimization in frequency domain. From extensive experimentations, it is shown that the proposed PSO based acoustic echo cancellation method provides high echo cancellation performance in terms of echo return loss enhancement with a faster convergence rate in comparison to that obtained by some of the state-of-the-art methods.