Self-adapting variable step size strategies for active noise control systems with acoustic feedback

While several proactive acoustic feedback (Larsen-effect) cancellation schemes have been presented for speech applications with short acoustic feedback paths as encountered in hearing aids, these schemes fail with the long impulse responses inherent to, for instance, public address systems. We derive a new prediction error method (PEM)-based scheme (referred to as PEM-AFROW) which identifies both the acoustic feedback path and the nonstationary speech source model. A cascade of a short- and a long-term predictor removes the coloring and periodicity in voiced speech segments, which account for the unwanted correlation between the loudspeaker signal and the speech source signal. The predictors calculate row operations which are applied to prewhiten the speech source signal, resulting in a least squares system that is solved recursively by means of normalized least mean square or recursive least squares algorithms. Simulations show that this approach is indeed superior to earlier approaches whenever long acoustic channels are dealt with

Acoustic Echo Cancelation is a typical event in today's telecommunication fields. The input speech signal interference caused by acoustic echo is disturbing for both users side and causes a reduction in the quality of the original signal. The adaptive filtering techniques are used to reduce the unwanted echo, and increasing quality of the speech signal. This Paper focus on different Adaptive filters algorithms and characterized by the Normalized Least Mean Square (NLMS) and Variable Step Size Least Mean Square (VSSLMS) and also This Paper actualizes Real Time Acoustic Echo Cancellation on Tms320c6713dsk. The primary modules of this reverberation canceller are a versatile channel framework utilizing Normalized Least Mean Square (NLMS) calculation, a Double-Talk Detector (DTD) and a Non-Linear Processor (NLP). The Setup of NLMS based versatile framework, Modified NLMS with Variable Step Size calculations and Frequency Domain based Fast LMS algorithms are to be actualized on the DSP equipment. This implementation shows fast response, speed, convergence of time and good tracking capability. The calculation of different adaptive algorithms must be analysed utilizing MATLAB.

This papers aims to develop an Adaptive Feedback Active Noise Control (AFB-ANC) system comprising two adaptive filters. The first adaptive filter is the same as found in the traditional AFB-ANC systems and is adapted using the classical Filtered-x Least Mean Square (FxLMS) algorithm. The stability of the FxLMS algorithm requires filtering the reference signal via a `good' estimate of the cancellation path (CP) present between the cancellation loudspeaker and the error microphone. Since the CP may be time-varying in the practical situations, the task of the second adaptive filter is to provide estimation of the CP during operation of the AFB-ANC system. The Cancellation-Path Estimation (CPE) filter is excited by a probe signal and is adapted using a delay-based Normalized Least Mean Square (NLMS) algorithm. The proposed system is equipped with a simple strategy to monitor the convergence status of the CPE filter. The developed strategy allows to inject a large (small) probe signal during the transient (steady) state of the AFB-ANC system. Computer simulation are carried out which demonstrate the effective performance of the developed system.

Publication in the conference proceedings of EUSIPCO, Antalya, Turkey, 2005

Acoustic feedback in hearing aids occurs due to the coupling between the hearing aid loudspeaker and microphones. In order to reduce acoustic feedback, adaptive filters are often used to estimate the feedback path. To increase the convergence speed and decrease the computational complexity of the adaptive algorithms, it has been proposed to split the acoustic feedback path into a time-invariant fixed part and a time-varying variable part. A key question of this approach is how to determine the fixed part. In this paper, two approaches are investigated: (1) a digital filter design approach that makes use of the signals of at least two hearing aid microphones and (2) a defined physical location approach using an electro-acoustic model and the signals of one hearing aid microphone and an additional ear canal microphone. An experimental comparison using measured acoustic feedback paths showed that both approaches enable one to reduce the number of variable part coefficients. It is shown that individualization of the fixed part increases the performance. Furthermore, the two approaches offer solutions for different requirements on the effort to a specific hearing aid design on the one hand and the effort during the hearing aid fitting on the other hand.

Feedback is a problem in hearing aids which will cause signal degradation and reduce the maximum applicable gain. More specifically, the advantages of open fittings (e.g., minimizing the occlusion effect) are limited by acoustic feedback. Feedback cancelation algorithms are used to overcome these limitations. For the development of such algorithms, the acoustic feedback path of the hearing aid must be known. The acoustic feedback path is not only affected by the outer sound field but by the individual anatomy and physiology as well. In order to quantify these different influences, feedback path measurements were performed on 20 human subjects. The measurements included different static conditions as well as dynamic ones (i.e., repetitive movements were performed during the measurement). Since the sound pressure level must be limited in measurements on human subjects, a valid identification of the feedback path is difficult in many cases, due to a low signal to noise ratio. Therefore, all measurements were performed reciprocally in addition to the direct measurements. Results show that yaw movements only have a small influence on the acoustic feedback path, whereas changes of the outer sound field can have a substantial impact on the feedback path.

Accurate model of secondary paths is very crucial for the multi-channel filtered-X least mean square algorithm applied in adaptive active noise control system. The auxiliary random noise technique is popular for online secondary path modeling during adaptive active noise control operation. This paper proposes a simplified variable step-size strategy and an effective auxiliary noise power scheduling strategy for the multi-channel filtered-X least mean square algorithm. Through a defined indirect error signal, the proposed method can guarantee every online secondary path modeling filter has its own exclusive variable step-size strategy to update their coefficients, and every injected noise has its own exclusive scheduling strategy considering all of the corresponding online secondary path modeling filters. The proposed method can improve the adaptive performance and simplifies the complexity of multi-channel adaptive active noise control system. Computer simulations show that the proposed method gives much better noise reduction and secondary path modeling accuracy at a somewhat faster convergence rate comparing with the competing methods.

In this paper, the characteristics of forces in active control systems connected to adjacent levels of a building are analyzed. The following characteristics are observed: (1) active control can provide significantly superior supplemental damping to a building, but causes a small frequency shift; (2) the linear quadratic regulator (LQR)-based control force is composed of an elastic restoring force component and a damping force component, where the damping force is almost identical to the total control force, however, the elastic restoring force is very small; and (3) the active control forces prevent motion most of the time during the entire control process. These three characteristics imply that active control systems connected to adjacent levels of a building behave like passive damping devices with adjustable parameters, namely damping characteristics in an active control, which is the mechanism used by semi-active control devices to reach similar performance as active control systems. Two indices are defined to quantify the damping characteristics of control forces in active control systems. These two indices can also be used to quantify the capacity of semi-active control to achieve the performance of active control. Based on the above observations, two principles are founded for optimization of parameters of semi-active control devices and passive dampers. The first is that the maximum output force of a semi-active or passive device to be designed is identical to an active device, called “design principle”. The other is the response equivalent principle, which states that the response of a building with semi-active or passive devices is the same as with active devices when the same maximum output force is applied. The design procedure for semi-active control devices and passive dampers is described in detail. Finally, numerical simulations of two benchmark problems is conducted to demonstrate the damping characteristics of active control and investigate the capacity of semi-active control to achieve the same performance as active control.

Among all adaptive noise cancellers, Widrow and Hoff's least mean square (LMS) algorithm has probably become the most popular because of its robustness, good tacking properties and simplicity of implementation. An important limitation of LMS algorithm is that the selection of a certain value for the step size implies compromise between speed of convergence and steady-state misadjustment, The variable step size normalized least-mean-square (VSS- NLMS) algorithm is appropriate to solve the conflicting requirement of fast convergence and low misadjustment of the LMS algorithm. To reduce noise in the output signal of the ANC, a step size for coefficient update is controlled according to the signal-to-noise ratio (SNR). This paper investigates the performance of a noise canceller with DSP processor (TI TMS320C6713) using the LMS algorithm, normalized least-mean-square (NLMS) algorithm, and VSS-NLMS algorithm. Results show the proposed combination of hardware and VSS-NLMS algorithm has not only a faster convergence rate but also lower distortion when compared with the fixed step size LMS algorithm and NLMS algorithm in real-time environments.

The main focus of this paper is the acoustic feedback path neutralisation during online operation of the single‐channel active noise control (ANC) systems. Invasive techniques, in which additive auxiliary noise is injected for online feedback path modelling (FBPM), degrade the noise‐reduction performance of ANC systems. In the existing methods for online FBPM, additive auxiliary noise with fixed variance is injected during all operating conditions of ANC systems. In this paper, a scheduling strategy is proposed to have time‐varying gain for additive auxiliary noise. The time‐varying gain is computed on the basis of (i) the convergence status of the FBPM filter, and (ii) the power of the interference term in the error signal of the FBPM filter. Simulation results show that, compared to the existing methods, the proposed method can better achieve the conflicting requirements of fast convergence of the FBPM filter, and reduced steady‐state power of the residual error at the error microphone. For sake of completeness, appendix presents typical simulation results when both the secondary path and feedback path are simultaneously identified during online operation of ANC systems.

Variable step-size normalized LMS algorithm by approximating correlation matrix of estimation error

The adaptive feedback canceller (AFC) based on the least mean square (LMS) or normalized LMS (NLMS) algorithm shows poor convergence for sparse impulse response, since they do not consider the characteristics of the impulse response. The acoustic feedback path of the hearing aid exhibits sparse characteristics consisting of few active coefficients and many non active (near to zero) coefficient values. This paper proposes an improved gradient controlled improved proportionate NLMS (IGC-IPNLMS) algorithm for cancelling the feedback phenomenon in hearing aids. The IGC-IPNLMS algorithm employs a variable convergence factor, an estimate of the gradient vector for allocating step size, the controlling parameter of the IGC-IPNLMS algorithm is also updated in each iteration based on the sparseness measure. Further, the reduction in computational complexity is achieved by using sparse partial update method along with the IGC-IPNLMS algorithm (SIGC-IPNLMS) for feedback cancellation. The simulation results show the superior performance of the proposed method for white noise and speech segment as input.

Long impulse response system identification presents two challenges for standard normalized least mean square (NLMS) filtering method: heavy computational load and slow convergence. When the response is sparse, partial update algorithms can reduce the computational complexity, but most often at the expense of performance. This paper discusses the tap selection rule for partial update NLMS algorithm in the case of white Gaussian input. We consider output mean square error (MSE) minimization based on gradient analysis and propose an algorithm that switches tap selection criterion between the one based on filter coefficient magnitudes and the one based on input magnitudes. We show that for identifying sparse systems, the new algorithm can outperform standard NLMS significantly with a reduced computational load.

This paper presents an improvement to ordinary voice filters that are unable to remove low frequency noise. This problem occurs because their filter parameters are over tuned, thereby destroying the speech characteristics. The proposed scheme combines NLMS, Fuzzy logic and Kalman filtering to restrain the background noise and keep the speech characteristics. This scheme is called the Normalized Fuzzy Logic Kalman Filter (NFLKF). It is especially effective when speech signals are collected in a noisy environment. Here, the output signal of the Kalman filtering is analyzed with the normalized LMS to obtain the coefficient, sigma <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> , and is also analyzed with the fuzzy logic to obtain the threshold, f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> . Then, sigma <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> and f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> are used together to adjust the Kalman filter parameters. This scheme can restrain the noise and improve the signal-to-noise ratio. The empirical validation is done by comparing the spectrogram from our scheme with the spectrograms from other filtering schemes, including the Normalized Least Mean Square (NLMS) Filter, the Kalman Filter and the Recursive Least Square Filter (RLS). The results show that the filtering schemes proposed can indeed restrain medium and low frequency noises which are usually difficult to handle, and does not compromise the speech characteristic. Therefore, a better signal-noise ratio is obtained and the speech quality is enhanced.

A novel bilinear functional link neural network filter for nonlinear active noise control