Nonlinearity-robust linear acoustic echo canceller using the maximum Correntropy criterion

For the problem of acoustic echo cancellation (AEC) with nonlinear distortions, we propose to use a linear adaptive filter that maximizes the Correntropy similarity measure instead of the conventional minimization of the mean squared error (MSE) criterion. The maximum Correntropy criterion (MCC) offers robustness to outliers and impulsive noise, which is interesting for the case of speech signal coupled with nonlinearities. To assess the performance of the algorithm, we consider a hard-clipping memoryless saturation nonlinearity. Our simulation results show very interesting performance of the normalized MCC-based linear adaptive filter for the echo return loss enhancement (ERLE) and misalignment measures compared to the MSE-based normalized least mean squares (NLMS) approach. Furthermore, the NMCC adaptive filter has a similar computational complexity as the NLMS algorithm, which makes it very attractive in practical implementations

System approach to robust acoustic echo cancellation through semi-blind source separation based on independent component analysis

We live in a dynamic world full of noises and interferences. The conventional acoustic echo cancellation (AEC) framework based on the least mean square (LMS) algorithm by itself lacks the ability to handle many secondary signals that interfere with the adaptive filtering process, e.g., local speech and background noise. In this dissertation, we build a foundation for what we refer to as the system approach to signal enhancement as we focus on the AEC problem. We first propose the residual echo enhancement (REE) technique that utilizes the error recovery nonlinearity (ERN) to "enhances" the filter estimation error prior to the filter adaptation. The single-channel AEC problem can be viewed as a special case of semi-blind source separation (SBSS) where one of the source signals is partially known, i.e., the far-end microphone signal that generates the near-end acoustic echo. SBSS optimized via independent component analysis (ICA) leads to the system combination of the LMS algorithm with the ERN that allows for continuous and stable adaptation even during double talk. Second, we extend the system perspective to the decorrelation problem for AEC, where we show that the REE procedure can be applied effectively in a multi-channel AEC (MCAEC) setting to indirectly assist the recovery of lost AEC performance due to inter-channel correlation, known generally as the "non-uniqueness" problem. We develop a novel, computationally efficient technique of frequency-domain resampling (FDR) that effectively alleviates the non-uniqueness problem directly while introducing minimal distortion to signal quality and statistics. We also apply the system approach to the multi-delay filter (MDF) that suffers from the inter-block correlation problem. Finally, we generalize the MCAEC problem in the SBSS framework and discuss many issues related to the implementation of an SBSS system. We propose a constrained batch-online implementation of SBSS that stabilizes the convergence behavior even in the worst case scenario of a single far-end talker along with the non-uniqueness condition on the far-end mixing system. The proposed techniques are developed from a pragmatic standpoint, motivated by real-world problems in acoustic and audio signal processing. Generalization of the orthogonality principle to the system level of an AEC problem allows us to relate AEC to source separation that seeks to maximize the independence, hence implicitly the orthogonality, not only between the error signal and the far-end signal, but rather, among all signals involved. The system approach, for which the REE paradigm is just one realization, enables the encompassing of many traditional signal enhancement techniques in analytically consistent yet practically effective manner for solving the enhancement problem in a very noisy and disruptive acoustic mixing environment.PhDCommittee Chair: Biing-Hwang Juang; Committee Member: Brani Vidakovic; Committee Member: David V. Anderson; Committee Member: Jeff S. Shamma; Committee Member: Xiaoli M

주파수 및 시간적 상관관계에 기반한 음향학적 에코 억제 기법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 8. 김남수.In the past decades, a number of approaches have been dedicated to acoustic echo cancellation and suppression which reduce the negative effects of acoustic echo, namely the acoustic coupling between the loudspeaker and microphone in a room. In particular, the increasing use of full-duplex telecommunication systems has led to the requirement of faster and more reliable acoustic echo cancellation algorithms. The solutions have been based on adaptive filters, but the length of these filters has to be long enough to consider most of the echo signal and linear filtering in these algorithms may be limited to remove the echo signal in various environments. In this thesis, a novel stereophonic acoustic echo suppression (SAES) technique based on spectral and temporal correlations is proposed in the short-time Fourier transform (STFT) domain. Unlike traditional stereophonic acoustic echo cancellation, the proposed algorithm estimates the echo spectra in the STFT domain and uses a Wiener filter to suppress echo without performing any explicit double-talk detection. The proposed approach takes account of interdependencies among components in adjacent time frames and frequency bins, which enables more accurate estimation of the echo signals. Due to the limitations of power amplifiers or loudspeakers, the echo signals captured in the microphones are not in a linear relationship with the far-end signals even when the echo path is perfectly linear. The nonlinear components of the echo cannot be successfully removed by a linear acoustic echo canceller. The remaining echo components in the output of acoustic echo suppression (AES) can be further suppressed by applying residual echo suppression (RES) algorithms. In this thesis, we propose an optimal RES gain estimation based on deep neural network (DNN) exploiting both the far-end and the AES output signals in all frequency bins. A DNN structure is introduced as a regression function representing the complex nonlinear mapping from these signals to the optimal RES gain. Because of the capability of the DNN, the spectro-temporal correlations in the full-band can be considered while finding the nonlinear function. The proposed method does not require any explicit double-talk detectors to deal with single-talk and double-talk situations. One of the well-known approaches for nonlinear acoustic echo cancellation is an adaptive Volterra filtering and various algorithms based on the Volterra filter were proposed to describe the characteristics of nonlinear echo and showed the better performance than the conventional linear filtering. However, the performance might be not satisfied since these algorithms could not consider the full correlation for the nonlinear relationship between the input signal and far-end signal in time-frequency domain. In this thesis, we propose a novel DNN-based approach for nonlinear acoustic echo suppression (NAES), extending the proposed RES algorithm. Instead of estimating the residual gain for suppressing the nonlinear echo components, the proposed algorithm straightforwardly recovers the near-end speech signal through the direct gain estimation obtained from DNN frameworks on the input and far-end signal. For echo aware training, a priori and a posteriori signal-to-echo ratio (SER) are introduced as additional inputs of the DNN for tracking the change of the echo signal. In addition, the multi-task learning (MTL) to the DNN-based NAES is combined to the DNN incorporating echo aware training for robustness. In the proposed system, an additional task of double-talk detection is jointly trained with the primary task of the gain estimation for NAES. The DNN can learn the good representations which can suppress more in single-talk periods and improve the gain estimates in double-talk periods through the MTL framework. Besides, the proposed NAES using echo aware training and MTL with double-talk detection makes the DNN be more robust in various conditions. The proposed techniques show significantly better performance than the conventional AES methods in both single- and double-talk periods. As a pre-processing of various applications such as speech recognition and speech enhancement, these approaches can help to transmit the clean speech and provide an acceptable communication in full-duplex real environments.Chapter 1 Introduction 1 1.1 Background 1 1.2 Scope of thesis 3 Chapter 2 Conventional Approaches for Acoustic Echo Suppression 7 2.1 Single Channel Acoustic Echo Cancellation and Suppression 8 2.1.1 Single Channel Acoustic Echo Cancellation 8 2.1.2 Adaptive Filters for Acoustic Echo Cancellation 10 2.1.3 Acoustic Echo Suppression Based on Spectral Modication 11 2.2 Residual Echo Suppression 13 2.2.1 Spectral Feature-based Nonlinear Residual Echo Suppression 15 2.3 Stereophonic Acoustic Echo Cancellation 17 2.4 Wiener Filtering for Stereophonic Acoustic Echo Suppression 20 Chapter 3 Stereophonic Acoustic Echo Suppression Incorporating Spectro-Temporal Correlations 25 3.1 Introduction 25 3.2 Linear Time-Invariant Systems in the STFT Domain with Crossband Filtering 26 3.3 Enhanced SAES (ESAES) Utilizing Spectro-Temporal Correlations 29 3.3.1 Problem Formulation 31 3.3.2 Estimation of Extended PSD Matrices, Echo Spectra, and Gain Function 34 3.3.3 Complexity of the Proposed ESAES Algorithm 36 3.4 Experimental Results 37 3.5 Summary 41 Chapter 4 Nonlinear Residual Echo Suppression Based on Deep Neural Network 43 4.1 Introduction 43 4.2 A Brief Review on RES 45 4.3 Deep Neural Networks 46 4.4 Nonlinear RES using Deep Neural Network 49 4.5 Experimental Results 52 4.5.1 Combination with Stereophonic Acoustic Echo Suppression 59 4.6 Summary 61 Chapter 5 Enhanced Deep Learning Frameworks for Nonlinear Acoustic Echo Suppression 69 5.1 Introduction 69 5.2 DNN-based Nonlinear Acoustic Echo Suppression using Echo Aware Training 72 5.3 Multi-Task Learning for NAES 75 5.4 Experimental Results 78 5.5 Summary 82 Chapter 6 Conclusions 89 Bibliography 91 요약 101Docto

Generalized Minimum Error Entropy for Adaptive Filtering

Error entropy is a important nonlinear similarity measure, and it has received increasing attention in many practical applications. The default kernel function of error entropy criterion is Gaussian kernel function, however, which is not always the best choice. In our study, a novel concept, called generalized error entropy, utilizing the generalized Gaussian density (GGD) function as the kernel function is proposed. We further derivate the generalized minimum error entropy (GMEE) criterion, and a novel adaptive filtering called GMEE algorithm is derived by utilizing GMEE criterion. The stability, steady-state performance, and computational complexity of the proposed algorithm are investigated. Some simulation indicate that the GMEE algorithm performs well in Gaussian, sub-Gaussian, and super-Gaussian noises environment, respectively. Finally, the GMEE algorithm is applied to acoustic echo cancelation and performs well.Comment: 9 pages, 8 figure

Aplicación de algoritmos combinados de filtrado adaptativo a acústica de salas

Las aplicaciones de procesamiento de señales acústicas están cobrando una importancia creciente. La mayoría de aplicaciones de este tipo (como la cancelación de eco acústico, la cancelación de ruido, la dereverberación, la separación y el seguimiento de fuentes acústicas, etc.) requieren la identificación de una (o varias) respuestas al impulso del recinto (RIRs). Estas respuestas pueden variar con el tiempo, por lo que se precisa de esquemas adaptativos para su identificación. La utilización de esquemas adaptativos en escenarios de identificación de respuestas acústicas se ve sujeta a diferentes compromisos, como, p. ej., la conocida relación entre velocidad de convergencia y precisión en estacionario. Varios de estos compromisos se comparten con otras aplicaciones, mientras que otros son específicos del procesamiento de señales acústicas. Entre los diferentes métodos que tratan de aliviar estas limitaciones, destaca la combinación adaptativa de filtros adaptativos debido fundamentalmente a su sencillez, versatilidad y eficacia. En esta Tesis Doctoral se aborda el estudio, diseño, implementación y adecuación de los esquemas de combinación adaptativa para que resulten provechosos y convenientes en aplicaciones de procesamiento de señales acústicas. Para ello, se proponen y analizan esquemas de combinación que ofrecen robustez y un comportamiento adecuado con respecto a las particularidades que presentan las señales acústicas involucradas y las RIRs. De entre los posibles condicionantes y sus potenciales soluciones, en esta Tesis Doctoral se contemplan: - La relación señal a ruido es normalmente desconocida a priori y puede variar. Se han desarrollado dos esquemas de combinación de filtros robustos frente a cambios en dicha relación. - El espectro de las señales acústicas (música y voz) no es plano en frecuencia, lo que ralentiza la convergencia de los filtros adaptativos. Se presenta un algoritmo de combinación en el dominio frecuencial que permite combinar de forma independiente diferentes bandas de frecuencia, obteniendo ganancias debido a que, por lo general, la relación señal a ruido es diferente en cada subbanda, y los cambios producidos en la RIR no afectan de igual forma a todo el margen frecuencial. En algunos casos, la relación entre la señal a reproducir por los altavoces y la captada por los transductores receptores es no lineal. La solución estándar para este problema de identificación no lineal se basa normalmente en los filtros de Volterra, y esta Tesis Doctoral presenta dos novedosas estrategias de combinación ad-hoc para su utilización en este contexto, las cuales obtienen ventajas de las particularidades de este tipo de filtros. Además, se propone un esquema que presenta una gran robustez con respecto a la ausencia o presencia de distorsión no lineal, e incluso con respecto a variaciones en la potencia de esta distorsión, con un modesto incremento de coste computacional con respecto al de un filtro de Volterra clásico. En muchas ocasiones, la longitud de la RIR es grande y la distribución de su energía no uniforme. Se propone un esquema que, explotando el compromiso entre sesgo y varianza, permite ganancias en esta situación, principalmente cuando la relación señal a ruido es baja. Para mostrar las ventajas del uso de los esquemas de combinación propuestos, se han llevado a cabo una serie de experimentos utilizando un escenario de cancelación de eco acústico monocanal. En todos los casos, las soluciones presentadas han obtenido resultados satisfactorios, demostrando la versatilidad y el potencial de estos algoritmos, y permitiendo mejorar el funcionamiento de los filtros adaptativos ante los condicionantes anteriormente citados. ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Acoustic signal processing applications are becoming increasingly important. Most of these applications, such as acoustic echo cancellation, noise cancellation, dereverberation, separation and tracking of acoustic sources, etc., requires the identification of a (or several) room impulse response (RIR). This response is usually time-varying, what justifies the use of adaptive algorithms to carry out the identification task. The use of adaptive schemes in RIR identification scenarios is subject to different compromises, such as the well-known compromise between speed of convergence and steady-state precision. Several of these tradeoffs are shared by other applications, while others are specific to acoustic signal processing. Among the different methods available to alleviate these limitations, adaptive combination of adaptive filters has been recently receiving a lot of attention, mainly because of its simplicity, versatility, and effectiveness. In this Ph. D. Thesis, we deal with the development, study and implementation of adaptive combination schemes that are especially suited to acoustic signal processing applications. For this purpose, we propose and analyze combination schemes that offer robustness and a suitable behavior with respect to the peculiarities of the involved signals and RIRs. Among all possible determining factors and their potential solutions, in this Ph. D. Thesis we consider: The signal to noise ratio is usually unknown a priori and it can be time-varying. In order to deal with this situation, two new different schemes are proposed. The spectrum of acoustic signals (music and speech) is not flat, what slows down the convergence of adaptive filters. We present a combination algorithm in the frequency domain that allows to mix different frequency bands independently, offering gains that exploit the frequency dependent signal to noise power ratio and the fact that RIR changes can also take place in a frequency-localized manner. Occasionally, the relationship between the signal to be reproduced by the loudspeakers and the signal received by the microphones is nonlinear. The standard solution for this nonlinear identification problem is frequently based on Volterra filters. The Thesis presents two novel ad-hoc combinations strategies to be used in this context, which take advantage of the particularities of this kind of filters. In addition, we propose an additional algorithm that shows great robustness with respect to the presence or absence of nonlinear distortion, and even with respect to changes in the power of nonlinear distortion, with a very modest increment in terms of computational cost. In many cases, very large RIRs are present, and their energies are typically distributed in a non-uniform manner. We propose a scheme that, exploiting the tradeoff between bias and variance, permits important gains in this situation, mainly for low signal to noise power ratios. In order to illustrate the advantages of the proposed combinations schemes, several experiments have been carried out considering a single-channel acoustic echo cancellation scenario. The satisfactory results obtained by the presented solutions demonstrate the versatility and potential of these algorithms, allowing to improve the performance of adaptive filters in the presence of the aforementioned conditions

Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)

Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression