Echo Cancellation Research Articles

Frequency domain exponential functional link network filter: Design and implementation

The exponential functional link network (EFLN) filter has attracted tremendous interest due to its enhanced nonlinear modeling capability. However, the computational complexity will dramatically increase with the dimension growth of the EFLN-based filter. To improve the computational efficiency, we propose a novel frequency domain exponential functional link network (FDEFLN) filter in this paper. The idea is to organize the samples in blocks of expanded input data, transform them from time domain to frequency domain, and thus execute the filtering and adaptation procedures in frequency domain with the overlap-save method. A FDEFLN-based nonlinear active noise control (NANC) system has also been developed to form the frequency domain exponential filtered-s least mean-square (FDEFsLMS) algorithm. Moreover, the stability, steady-state performance and computational complexity of algorithms are analyzed. Finally, several numerical experiments corroborate the proposed FDEFLN-based algorithms in nonlinear system identification, acoustic echo cancellation and NANC implementations, which demonstrate much better computational efficiency.

Howling Detection and Suppression Based on Segmented Notch Filtering.

The existing adaptive echo cancellation based howling (typically in hearing aids) removal methods have several drawbacks such as insufficient attenuation of the howling component, slow response and nonlinear distortion. To solve these problems, we propose a segmented notch filtering based scheme. Specifically, firstly, it is proved that the attenuation value can reach dB at any detected howling frequency; secondly, the filter coefficients can be readily calculated by a closed-form formula, yielding a fast response to the sudden howling accident; thirdly, the closed-form formula of this filter is theoretically an even function, indicating that this filter possesses a linear transfer characteristic. In combination with proper segmentation and precisely removing these transient samples arising from FIR (Finite Impulsive Response) filtering, nonlinear distortion can be entirely avoided. Experimental results show that our proposed scheme can not only accurately estimate the howling frequency, but can also completely remove it, which yields a high-quality output waveform with a recovery SNR of about 22 dB. Therefore, the proposed segmented notching based scheme possesses vast potential for hearing aid development and other relevant applications.

Affine Projection Algorithm for Censored Regression

Non-Gaussian colored input signal and censored observation are encountered frequently in many practical applications of adaptive signal processing. Conventional adaptive algorithms will face convergence performance degradation in such cases. To address the issue, the affine projection algorithm for censored regression (CR-APA) is proposed in the paper. The censored thresholds and the variance of the background noise are regarded as the prior knowledge to compensate for the bias caused by censored observation. By theoretical analysis, the range of the step size is derived to guarantee the stabilization of the CR-APA. Besides, the method of evolving projection order is employed to improve the convergence performance of the CR-APA. Computer simulations in system identification and echo cancellation applications are performed to demonstrate the better convergence performance of the proposed algorithms in censored data processing over competing algorithms, and to verify the improvement of the CR-APA by the method of evolving projection order. The proposed CR-APA and CR-EAPA can be used in engineering applications to make up for the hardware.

Review Paper on Innovation of Advanced Noise Reduction Technique for Better Audibility Process

Abstract: Now a days there are many people affected by hearing loss that make them disabled as they cannot communicate properly .The main complaint of people with hearing loss is low ability to deduce speech in a noisy environment. Hearing aid is a delicate instrument, which can acquire, process and feedback realistic signal in real time. In this matter various apparent opposition matching algorithm, various filtering methods, digital signal processing algorithm and echo cancellation are developed and implemented. The purpose of this object is to develop the digital signal processing based platform for digital hearing aid technique, which is for the people with hearing impairment using the low cost fuzzy orange pi model. To Perform this Application fuzzy algorithm is used which is quite easy to implement and required less operative computation. The algorithms are performed using MATLAB language which gives the best clarity and simulated functionality over MATLAB. Keywords: Speech Recognition, Noise Reduction, SNR, Fuzzy Masking Technique

Variable Step‐Size Correntropy‐Based Affine Projection Algorithm with Compound Inverse Proportional Function for Sparse System Identification

AbstractIn view of the poor performance of the traditional adaptive filtering algorithm in identifying sparse systems under impulse noise, an affine projection maximum correntropy criterion with compound inverse proportional function (APMCCCIPF) algorithm is proposed in this paper, and the compound inverse proportional function approaching norm is introduced into the traditional affine projection maximum correntropy criterion (APMCC) algorithm. The fixed step‐size APMCCCIPF algorithm cannot obtain faster convergence speed and lower steady‐state error at the same time, and the most effective method to solve this contradiction is to change the fixed step‐size in the algorithm to a variable step‐size. Further, a variable step‐size factor is constructed in the APMCCCIPF algorithm, and a novel variable step‐size APMCCCIPF algorithm is proposed. A modified Gaussian function, which can resist impulse noise interference, is applied to adjust the step‐size change. Besides, the convergence and complexity of the proposed algorithms are analyzed. In the simulation of sparse system identification and echo cancellation under impulsive noise, the proposed algorithms not only achieve a lower and faster convergence rate, but also obtain a lower steady‐state error. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Nonlinear stereophonic acoustic echo cancellation using sub-filter based adaptive algorithm

The nonlinear echo path resulting from the unwarranted acoustic echo and nonlinear distortion will degrade the echo cancellation performance of a stereophonic acoustic echo cancellation system. Techniques proposed in the literature rely on the assumption of a linear echo path. In this paper, a nonlinear stereophonic acoustic echo cancellation framework is proposed to address the problem of nonlinear distortion in stereophonic acoustic cancellation systems with a better trade-off between convergence and steady-state performance. The proposed approach uses the functional link-based adaptive filtering to deal with the nonlinear distortion in combination with the sub-filter approach to enhance the convergence performance. In addition to that, the convergence analysis of the proposed algorithm is presented in this paper. Simulations carried out using echo return loss enhancement and magnitude squared coherence as performance metrics using speech and colored noise input demonstrate the proposed framework's ability to improve the echo cancellation performance in the presence of distortion.

High-performance and ultra-compact spike-based architecture for real-time acoustic echo cancellation

In recent years, the implementation of advanced adaptive filters in embedded devices for acoustic echo cancellation has increased because most of them are used in portable devices, especially in Internet of Things systems, in which high-performance and low-area digital hardware implementations are required. In this work, we present a high-performance and ultra-compact spike-based hardware architecture to efficiently compute adaptive filters to be used as an acoustic echo canceller (AEC). To achieve this architecture, we address two factors. (1) We propose a new data-selective adaptive filter along with subband decomposition least mean square (LMS) method to reduce the computational cost by minimizing the number of operations required to efficiently update the filter coefficients. The proposed method requires approximately 40% fewer updates when compared with conventional subband adaptive filters. As a consequence, the spike-based hardware architecture simulates the proposed adaptive filter at high processing speeds. (2) We propose a compact and high-precision neural multiplier since acoustic echo cancellers require a large number of high-precision multiplications to efficiently identify the echo path. The proposed neural multiplier expends up to 15 times fewer synapses when compared with existing approaches, which represents a significant improvement in terms of area. In addition, this improvement avoids routing problems by implementing large-scale synapse connectivity in advanced FPGAs. Herein, we carry out exhaustive testing by simulating several acoustic echo scenarios. The results demonstrate that the features of the model and the hardware implementation techniques potentially allow easy integration into portable devices for use in real-world acoustic echo cancellation scenarios.

A Memory Sparse Proportionate Affine Projection Algorithm for Echo Cancellation: Analysis and Simulations

A Memory Sparse Proportionate Affine Projection Algorithm for Echo Cancellation: Analysis and Simulations

Reference-free AEC for Alexa Barge-In

When an Alexa built-in device is in playback mode, users need to make extra effort to cut through acoustic echo from the audio playback (music or synthesized speech signals) to activate the wakeword detector and start talking to Alexa, i.e., to barge into the conversation. These are high-friction events where Alexa customers typically experience a higher false reject rate (FRR). The acoustic echo canceller (AEC) and beamformer in the multichannel Audio Front End (AFE) are essential to mitigate the effect. But when playback volume is high, AFE outputs still contain strong residual echoes which downstream wakeword detection models could have difficulty to handle. In this talk, we will present an innovative multi-microphones yet array-agnostic, reference-free AEC tailored specifically for wakeword detection. As opposed to the traditional AEC, the new approach has five practically attractive benefits: (1) reference-free and array agnostic (a blind method suitable for in-model implementations); (2) inherently immune to loudspeaker nonlinearities; (3) free of synchronization/alignment hassle; (4) more microphones, more gains; (5) kills two birds with one stone (cancels both echo and noise). A proof-of-concept experiment will be discussed to validate the effectiveness of the proposed novel algorithm.

Nonlinear residual echo suppression based on dual-stream DPRNN

The acoustic echo cannot be entirely removed by linear adaptive filters due to the nonlinear relationship between the echo and the far-end signal. Usually, a post-processing module is required to further suppress the echo. In this paper, we propose a residual echo suppression method based on the modification of dual-path recurrent neural network (DPRNN) to improve the quality of speech communication. Both the residual signal and the auxiliary signal, the far-end signal or the output of the adaptive filter, obtained from the linear acoustic echo cancelation are adopted to form a dual-stream for the DPRNN. We validate the efficacy of the proposed method in the notoriously difficult double-talk situations and discuss the impact of different auxiliary signals on performance. We also compare the performance of the time domain and the time-frequency domain processing. Furthermore, we propose an efficient and applicable way to deploy our method to off-the-shelf loudspeakers by fine-tuning the pre-trained model with little recorded-echo data.

SFCW Radar with an Integrated Static Target Echo Cancellation System.

Continuous Wave (CW) radars systems, especially air-coupled Ground-Penetrating Radar (GPR) or Through-Wall Imaging Radar (TWIR) systems, echo signals reflected from a stationary target with high energy, which may cause receiver saturation. Another effect caused by reflection of stationary targets is noticeable as background within a radargram. Nowadays, radar systems use automatic gain control to prevent receiver saturation. This paper proposes a method to remove stationary targets automatically from the received signal. The method was designed for a radar system with a moving platform, with an assumption that the distance between the surface and target is constant. The design is proposed of an SFCW radar with an integrated system for real-time multiple static target Echo Cancellation (EC). The proposed EC system removes the static target using active Integrated Circuit (IC) components, which generate the corresponding EC signal for each frequency step of the SFCW radar and sum it with the received echo signal. This has the main advantage of removing even multiple echoes at any distance, and excludes the need for a high-dynamic-range receiver. Additionally, the proposed system has minimal impact on the radar size and power consumption. Besides static target removal, the antenna coupling can be removed if the signal appears to be constant. The operating frequency was selected between 500 MHz and 2.5 GHz, due to the limitation of the used electronic components. The experimental results show that the simulated target’s echo using a cable with a known length could be suppressed to up to 38 dB. Experimental results using a moving radar platform and the real environment scenario with static and dynamic targets, show that the proposed EC system could achieve up to 20 dB attenuation of the static target. The system does not affect any other target of interest, which can even move at any distance during the measurement. Therefore, this could be a promising method for further compact implementation into SFCW radars, or any other radar type that generates CW single frequencies.

An online algorithm for echo cancellation, dereverberation and noise reduction based on a Kalman-EM Method

Many modern smart devices are equipped with a microphone array and a loudspeaker (or are able to connect to one). Acoustic echo cancellation algorithms, specifically their multi-microphone variants, are essential components in such devices. On top of acoustic echos, other commonly encountered interference sources in telecommunication systems are reverberation, which may deteriorate the desired speech quality in acoustic enclosures, specifically if the speaker distance from the array is large, and noise. Although sub-optimal, the common practice in such scenarios is to treat each problem separately. In the current contribution, we address a unified statistical model to simultaneously tackle the three problems. Specifically, we propose a recursive EM (REM) algorithm for solving echo cancellation, dereverberation and noise reduction. The proposed approach is derived in the short-time Fourier transform (STFT) domain, with time-domain filtering approximated by the convolutive transfer function (CTF) model. In the E-step, a Kalman filter is applied to estimate the near-end speaker, based on the noisy and reveberant microphone signals and the echo reference signal. In the M-step, the model parameters, including the acoustic systems, are inferred. Experiments with human speakers were carried out to examine the performance in dynamic scenarios, including a walking speaker and a moving microphone array. The results demonstrate the efficiency of the echo canceller in adverse conditions together with a significant reduction in reverberation and noise. Moreover, the tracking capabilities of the proposed algorithm were shown to outperform baseline methods.

A full‐duplex transceiver for 20‐Gbps high‐speed simultaneous bidirectional signaling across global on‐chip interconnections

AbstractThis paper presents a high‐speed simultaneous bidirectional transceiver (SBT) for on‐chip wireline communications. A MOS hybrid transistor is utilized to split the received data from the superimposed signal at both ends of the on‐chip interconnection with the assistance of two drivers, namely main and auxiliary. Moreover, a high‐pass filter (HPF) is used as a differentiator to generate the echo cancelation signal. Consequently, the echo‐cancelation for simultaneous bidirectional signaling (SBS) is realized by the combination of the hybrid device and the differentiator. The proposed SBT has been designed and evaluated using 28‐nm CMOS technology over a narrow 5‐mm on‐chip interconnection, which possesses 11.9‐dB loss at the Nyquist frequency (half a bit rate). The energy‐efficiency of the proposed full‐duplex transceiver (FDT) for 20‐Gbps simultaneous bidirectional data transmission is 0.147 PJ/b/mm. The performance results show that the proposed SBT has better overall performance compared to the previous architectures reported in the literature to date. The layout of the presented SBT occupies a low area of 1574 μm2.

Normalized Combinations of Proportionate Affine Projection Sign Subband Adaptive Filter

The proportionate affine projection sign subband adaptive filter (PAP-SSAF) has a better performance than the affine projection sign subband adaptive filter (AP-SSAF) when we eliminate the echoes. Still, the robustness of the PAP-SSAF algorithm is insufficient under unknown environmental conditions. Besides, the best balance remains to be found between low steady-state misalignment and fast convergence rate. In order to solve this problem, we propose a normalized combination of PAP-SSAF (NCPAP-SSAF) based on the normalized adaption schema. In this paper, a power normalization adaptive rule for mixing parameters is proposed to further improve the performance of the NCPAP-SSAF algorithm. By using Nesterov’s accelerated gradient (NAG) method, the mixing parameter of the control combination can be obtained with less time consumed when we take the l1-norm of the subband error as the cost function. We also test the algorithmic complexity and memory requirements to illustrate the rationality of our method. In brief, our study contributes a novel adaptive filter algorithm, accelerating the convergence speed, reducing the steady-state error, and improving the robustness. Thus, the proposed method can be utilized to improve the performance of echo cancellation. We will optimize the combination structure and simplify unnecessary calculations to reduce the algorithm’s computational complexity in future research.

Path-Finder Optimization Based Control of Grid-Tied PV Hybrid Energy Storage System

This paper presents a path-finder algorithm (PFA) of meta-heuristic optimization for the performance enhancement of the bias compensated normalized maximum correntropy criteria (BCNMCC) based voltage source converter (VSC) control for a three-phase three-wire grid-tied dual-stage PV system. The DC link voltage variations during induced dynamic conditions are minimized by PFA optimized PI controller which also generates an accurate loss component of current for enhancing the VSC control performance. The BCNMCC algorithm effectively performs the system identification and echo cancellation in the presence of Gaussian noises. An accurate improves the VSC ability to accurately extract the fundamental load current component and to produce precise weight signals. The BCNMCC performs multifunctional operations, i.e., reactive power compensation, harmonics elimination, load balancing, and power balancing at point of common coupling during steady-state and various dynamic conditions, i.e., grid-islanding & re-synchronization, irradiation variation, unbalanced load and fixed power mode. The proposed system is capable of operating under grid-tied and isolated mode due to the seamless transition of a VSC control from grid current control (GCC) to voltage control. The seamless transition of VSC control does not induce any major transients at the source and on the load side of the system. The hybrid energy storage system (HESS) with a battery, fuel-cell and ultra-capacitor further improves the reliability of the PV system and the stability of the overall system during both grid-tied and isolated operations. The proposed system performs satisfactorily under steady-state and diverse dynamic states as per IEEE519 standards.

Deep learning-based stereophonic acoustic echo suppression without decorrelation.

Traditional stereophonic acoustic echo cancellation algorithms need to estimate acoustic echo paths from stereo loudspeakers to a microphone, which often suffers from the nonuniqueness problem caused by a high correlation between the two far-end signals of these stereo loudspeakers. Many decorrelation methods have already been proposed to mitigate this problem. However, these methods may reduce the audio quality and/or stereophonic spatial perception. This paper proposes to use a convolutional recurrent network (CRN) to suppress the stereophonic echo components by estimating a nonlinear gain, which is then multiplied by the complex spectrum of the microphone signal to obtain the estimated near-end speech without a decorrelation procedure. The CRN includes an encoder-decoder module and two-layer gated recurrent network module, which can take advantage of the feature extraction capability of the convolutional neural networks and temporal modeling capability of recurrent neural networks simultaneously. The magnitude spectra of the two far-end signals are used as input features directly without any decorrelation preprocessing and, thus, both the audio quality and stereophonic spatial perception can be maintained. The experimental results in both the simulated and real acoustic environments show that the proposed algorithm outperforms traditional algorithms such as the normalized least-mean square and Wiener algorithms, especially in situations of low signal-to-echo ratio and high reverberation time RT60.

Comparison of adaptive filtering scheme for sustainable and efficient communication in smart city

IoT is the big buzzword in today’s technical fraternity. The implementation of 5G networks is also up in urban areas. This is perhaps fortunate from an IoT perspective, as IoT’s main applications will realize Smart automated cities’ dream. Enhanced mobile broadband (eMBB)-based services are far and wide dependent upon the bandwidth requirement for effective communication in Smart City applications. Modern-day signal processing techniques are widely reliant on adaptive filters for efficient applications like signal prediction, echo cancellation, noise cancellation, etc. In this work, the use of adaptive filters is performed for stationary and non-stationary signals. It is well known that adaptive filters’ performance and effect depend on their regulating parameters and noise variance. Therefore, the parameter step size and filter orders are varied, and the outcome of the variations on respective signals is noted. Simulation of these scenarios is discussed at length to ensure better insight of adaptive filters to researchers and engineers to select the appropriate filter for a specific application.

Robust Normalized Subband Adaptive Filter Algorithm with a Sigmoid-Function-Based Step-Size Scaler and Its Convex Combination Version

In this paper, by inserting the logarithm cost function of the normalized subband adaptive filter algorithm with the step-size scaler (SSS-NSAF) into the sigmoid function structure, the proposed sigmoid-function-based SSS-NSAF algorithm yields improved robustness against impulsive interferences and lower steady-state error. In order to identify sparse impulse response further, a series of sparsity-aware algorithms, including the sigmoid L0 norm constraint SSS-NSAF (SL0-SSS-NSAF), sigmoid step-size scaler improved proportionate NSAF (S-SSS-IPNSAF), and sigmoid L0 norm constraint step-size scaler improved proportionate NSAF (SL0-SSS-IPNSAF), is derived by inserting the logarithm cost function into the sigmoid function structure as well as the L0 norm of the weight coefficient vector to act as a new cost function. Since the use of the fix step size in the proposed SL0-SSS-IPNSAF algorithm, it needs to make a trade-off between fast convergence rate and low steady-state error. Thus, the convex combination version of the SL0-SSS-IPNSAF (CSL0-SSS-IPNSAF) algorithm is proposed. Simulations in acoustic echo cancellation (AEC) scenario have justified the improved performance of these proposed algorithms in impulsive interference environments and even in the impulsive interference-free condition.

Double Talk Detection in hands-free mobile communication- A comprehensive survey

In modern teleconferencing systems, echo cancellation is a mandatory requirement. Echoes arise in Loudspeaker Enclosure Microphone devices because of coupling between loudspeaker and microphone signals. To remove these unwanted echoes from microphone signals, Acoustic Echo Cancellers are used. Several techniques exist for acoustic echo cancellation. Acoustic echo cancelling an adaptive FIR/IIR filter can predict the path of echo and cancel it. The adaptive algorithms give good performance when only the far-end signal is present. But when the near-end signal is present, the adaptive filter tends to diverge. Such a situation creates a double talk scenario. The filter adaptation must be prevented during the double talk period. Accurate detection of double talk is quite difficult and computationally complex. Hence the algorithms for detecting Double Talk are not easy to implement. Increased detection accuracy is achieved at the cost of computational complexity. Further, it is desired to cancel out the far end echo signal, thus giving a good quality of near-end speech signal as output. Here, we provide a detailed study of the different algorithms existing for double talk detection, and future direction of research in the field of double talk detection is proposed.

Multiple sub filter based proportionate filtering for nonlinear acoustic echo cancellation

This paper presents a multiple sub-filter-based improved nonlinear acoustic echo cancellation (NAEC) framework to enhance the echo cancellation performance of the NAEC in the presence of nonlinear distortion. The proposed algorithm uses a novel combination of adaptive multiple sub-filter approach and proportionate filtering to enhance the convergence rate of the existing proportionate functional link-based NAEC algorithm by reducing the adaption time needed for updating the coefficients of the adaptive filter. In addition to that, the convergence and the steady-state analysis of the proposed algorithm are presented. The proposed NAEC framework is subjected to speech signal input corrupted with both white as well as colored noise as background noise and the colored noise input at low-to–high SNR conditions for a comprehensive analysis of the echo cancellation performance. The experimental results comprising of the echo return loss enhancement, spectrograms, and the perceptual evaluation of speech quality demonstrate the improvements brought by the proposed algorithm. At all signal-to-noise ratio conditions, the proposed algorithm has shown a 4 dB improvement in mean echo return loss enhancement compared to the existing algorithms validating the improvement in the proposed NAEC scheme’s performance.