Gabor expansion for adaptive echo cancellation

Gabor's expansion and the Zak transform for continuous-time and discrete-time signals

Echo in telephones is generally undesirable but inevitable. Impedance mismatch and acoustic coupling between the receiver and microphone circuit(s) respectively produce electric and acoustic echoes in a telephone network. There is therefore a need to develop an echo canceller that can be used to reduce these echoes in telephone networks. In this work, a model for electrical echo in telephone networks for varied echo-path delay was designed. This was with a view to implementing an adaptive electrical echo canceller based on a combination of the normalized least mean square (NLMS) and Geigel speech detector algorithms (GSD). Subsequently, the performance indices (convergence rate and efficiency) of the echo canceller implemented were obtained using error function, echo return loss enhancement (ERLE) and the mean squared error (MSE) for various echoes with varied round-trip delay according to the minimum requirement of ITU-T (International Telecommunication Union) Recommendations. The results obtained show that there was a 3 dB improvement over the NLMS algorithm. The developed adaptive electrical echo canceller is therefore suitable for reduction of echoes in telephone networks.

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.

One of the most important applications of adaptive filter is Interference or noise cancellation. The objective of adaptive interference cancellation is to obtain an estimate of the interfering signal and to subtract it from the corrupted signal and hence obtain a noise free signal. The tracking performances of the LMS and NLMS algorithms are compared when the input of the adaptive filter is no stationary For this purpose, the filter uses an adaptive algorithm to change the value of the filter coefficients, so that it acquires a better approximation of the signal after each iteration. The LMS (Least Mean Square), and its variant the NLMS (Normalized LMS) are two of the adaptive algorithms widely in use. This paper presents a comparative analysis of the LMS and the NLMS in case of interference cancellation from speech signals. For each algorithm, the effects of two parameters-filter length and step size have been analyzed. Finally, the performances of the two algorithms in different cases have been compared.

A novel adaptive filter algorithm has been proposed that provides faster convergence than the least mean square (LMS) and normalized LMS (NLMS) algorithms. The NLMS algorithm is studied and modified so that the error sequence is used for designing the step size parameter at each iteration. A strategy to reduce the computational complexity the NLMS adaptation requires is used in the proposed algorithm. Computer simulations demonstrate that in the scenario of channel equalization, the proposed algorithm based on the structure of a transversal filter accomplishes faster start-up than the combined LMS/F algorithm recently addressed.

Adaptive filtering has been successfully used in canceling line echos in the telephone network since the 1960’s [179, 443, 387]. In these cases, the adaptive filter is used to model the unknown echo path in a communications channel and then cancel the echo signals from the conversation. An adaptive filter is required because the echo path is not only unknown, but also because it is different for each network path. However, the acoustic echo cancellation problem is not as easily solved with conventional adaptive filtering systems due to the requirements for a much longer adaptive impulse response and for faster convergence since the acoustic echo path is time-varying during the communication transmission. In this chapter, we summarize key results from adaptive filter theory, and discuss the least mean square (LMS) and normalized least mean square (NLMS) algorithms used to adjust an adaptive filter. We then examine adaptive filtering in subbands as one method for acoustic echo cancellation. Simulations (using a measured room response with speech signals) demonstrate that the oversampled subband adaptive system provides improved performance for acoustic echo cancellation applications.

A novel network echo canceler based on a square root free QR-decomposition-based least-squares lattice (SRF QRD-LSL) adaptive algorithm is presented. Specific characteristics of this type of algorithms are their high convergence rate, numerical stability and superior robustness to double-talk, which is not present in the case of the normalized least mean squares (NLMS) family of algorithms. This echo canceler is implemented on a powerful fixed-point digital signal processor (DSP), Motorola SC140. The experimental results prove superior performances over the classical echo cancelers based on NLMS algorithms.KeywordsAdaptive AlgorithmDigital Signal ProcessorAdaptive FilterAssembly CodeNormalize Little Mean SquareThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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 principal issue in acoustic echo cancellation (AEC) is to estimate the impulse response between the loudspeaker and the microphone of a hands-free communication device. Basically, we deal with a system identification problem, which can be solved by using an adaptive filter. The most common adaptive filters for AEC are the normalized least-mean-square (NLMS) algorithm and the affine projection algorithm (APA). These two algorithms have to compromise between fast convergence rate and tracking on the one hand and low misadjustment and robustness (against the near-end signal) on the other hand. In order to meet these conflicting requirements, the step-size parameter of the algorithms needs to be controlled. This is the motivation behind the development of variable step-size (VSS) algorithms. Unfortunately, most of these algorithms depend on some parameters that are not always easy to tune in practice. The goal of this chapter is to present a family of non-parametric VSS algorithms (including both VSS-NLMS and VSS-APA), which are very suitable for realistic AEC scenarios. They are developed based on another objective of AEC application, i.e., to recover the near-end signal from the error signal of the adaptive filter. As a consequence, these VSS algorithms are equipped with good robustness features against near-end signal variations, like double-talk. This idea can be extended even in the case of the recursive least-squares (RLS) algorithm, where the overall performance is controlled by a forgetting factor. Therefore, a variable forgetting factor RLS (VFF-RLS) algorithm is also presented. The simulation results indicate that these algorithms are reliable candidates for real-world applications.

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.

Rolling bearings are widely used in modern machinery and equipment, and the tough working environment is easy to cause their failure. To solve the problem of extracting fault signals of rolling bearings in a strong noise environment, a method based on Normalized Least Mean Square(NLMS) adaptive filtering and Ensemble Empirical Mode Decomposition(EEMD) noise reduction method is proposed. Firstly, NLMS is used to filter the signal, which is used for primary noise reduction. Then the signal is decomposed into a series of Intrinsic Mode Functions(IMFs) by EEMD, and the kurtosis value, root mean square value and sample entropy value of each IMF are calculated respectively. The appropriate one is selected according to the comprehensive index. Finally, the signal is reconstructed and the Hilbert transform is performed on the reconstructed signal to obtain the envelope spectrum, and the fault characteristic frequency is extracted. Simulation and experimental results show that the method can effectively reduce noise and successfully extract fault features.KeywordsNormalized least mean squareEnsemble empirical mode decompositionRolling bearingsFault diagnosisDenoising

Echo cancelers which cover longer impulse responses (/spl ges/64 ms) are desirable. Long responses create a need for more rapidly converging algorithms in order to meet the specifications for network echo cancelers devised by the ITU (International Telecommunication Union). In general, faster convergence implies a higher sensitivity to near-end disturbances, especially Previously, a fast converging algorithm called the proportionate NLMS (normalized least mean squares) algorithm (PNLMS) has been proposed. This algorithm exploits the sparseness of the echo path in order to increase the convergence rate. A robust version of PNLMS has also been presented which combines a double-talk detector with techniques from robust statistics to make the algorithm insensitive to double-talk. This paper presents a generalization of the robust PNLMS algorithm to a robust proportionate aAffine projection algorithm (APA) called PAPA that converges very fast.

This paper presents fixed-point dedicated architecture of NLMS (Normalized Least Mean Square) adaptive filtering algorithm for the harmonics power line interference cancelling. The NLMS is the normalized version of the LMS algorithm, whose main characteristics are related to the increase of speed of adaptation, and low sensitivity to the power reference signal. The developed architecture was described in VHDL and synthesized to FPGA. Synthesis results indicate that the interference canceller based on NLMS filter is able to efficiently cancel the interferences in the fundamental frequency, and its high order harmonics, with faster convergence speed than the previously proposed prominent solution that uses the LMS adaptive filter.

From the subscribes perspective, echo is arguably the worst type of impairment that can be encountered during a telephone conversation. The echo canceller's goal is to detect and remove echo as quickly and effectively, thereby minimizing any loss in voice quality. The echo canceller must perform this function under all conditions including double-talk (when both parties are speaking at the same time) and in the presence of background noise. Furthermore, the echo canceller must not cause detriment to signalling tones or fax and modem transmissions. In order to combat the echo phenomenon, an echo canceller is employed. Today's echo cancellers use sophisticated algorithms running on high speed Digital Signal Processors (DSPs) to combat the echo. The focus of this study is to develop an echo canceller by using Least Mean Square (LMS) adaptive filter. Simulink is used as the simulation tools, where the echo canceller can create a model of the echo path by sampling the voice signal that can estimates the echoes. This estimation subtracted from the incoming voice signal allowing normal speech to pass through. This is a very simplistic view, as echo cancellers need to have the capability to sample multiple echoes occurring at different times and at different levels.