Stereophonic acoustic echo cancellation employing selective-tap adaptive algorithms

Published versio

Stereophonic acoustic echo cancellation: Analysis of the misalignment in the frequency domain

Published versio

Applications of dynamic diffuse signal processing in sound reinforcement and reproduction.

Electroacoustic systems are subject to position-dependent frequency responses due to coherent interference between multiple sources and/or early reflections. Diffuse signal processing (DiSP) provides a mechanism for signal decorrelation to potentially alleviate this well-known issue in sound reinforcement and reproduction applications. Previous testing has indicated that DiSP provides reduced low-frequency spatial variance across wide audience areas, but in closed acoustic spaces is less effective due to coherent early reflections. In this paper, dynamic implementation of DiSP is examined, whereby the decorrelation algorithm varies over time, thus allowing for decorrelation between surface reflections and direct sounds. Potential applications of dynamic DiSP are explored in the context of sound reinforcement (subwoofers, stage monitoring) and sound reproduction (small-room low-frequency control, loudspeaker crossovers), with preliminary experimental results presented.N/

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

New Sequential Partial Update Normalized Least Mean M-estimate Algorithms for Stereophonic Acoustic Echo Cancellation

ABSTRACT This paper proposes a family of new robust adaptive filtering algorithms for stereophonic acoustic echo cancellation in impulsive noise environment. The new algorithms employ sequential partial update scheme to reduce computational complexity, which is desirable in long echo path case. On the other hand, by employing robust M-estimate technique, the new algorithms become more robust to impulsive noises compared to their conventional least squarebased counterparts. These two advantages enable the proposed algorithms to be good alternatives for stereophonic echo cancellation. Experiments are also conducted to verify their efficiency

Two-way acoustic window using wave field synthesis

Tässä diplomityössä esitellään monikanavainen ja kaksisuuntainen audiokommunikaatiojärjestelmä. Sen tavoitteena on luoda kaksisuuntainen akustinen avanne kahden tilan välille ja mahdollistaa tarkka äänilähteiden paikantuminen molemmissa tiloissa. Kun yksikanavainen kommunikaatiojärjestelmä laajennetaan monikanavaiseksi, on myös mahdollista parantaa puheen ymmärrettävyyttä. Toisaalta lisääntynyt kanavamäärä monimutkaistaa akustisen kierron poistamiseen käytettyjä tekniikoita. Tekniikat, jotka tunnetaan kaksikanavaisista järjestelmistä on mahdollista laajentaa myös monikanavaisiin järjestelmiin. Käyttämällä kaiutin- ja mikrofonihiloja on osittain mahdollista äänittää äänikenttä toisaalla ja toistaa se samanlaisena toisessa tilassa. Tämä voidaan toteuttaa tässä työssä käytetyllä menetelmällä, jota kutsutaan äänikenttäsynteesiksi. Akustisen kierron poistamiseksi toteutettiin 48-kanavainen järjestelmä, joka hyödynsi staattisten ja adaptiivisten suodinten yhdistelmää. Järjestelmä osoittautui stabiiliksi ja mahdollisti normaalin keskustelun rakennetun akustisen avanteen läpi. Aaltokenttäsynteesiä verrattiin muihin äänentoisto- ja äänitysjärjestelmiin kuuntelukokeiden avulla. Tulokset osoittavat, että äänikenttäsynteesin ominaisuudet ovat riittävät korkealaatuisen ja monikanavaisen äänikommunikaatiojärjestelmän toteuttamiseksi.In this Master's Thesis a two-way multichannel audio communication system is introduced. The aim is to create a virtual acoustic window between two rooms, providing correct spatial localization of multiple audio sources on both sides. Extending monophonic communication systems to feature multichannel sound capture and reproduction increases the intelligibility of speech and the accuracy of source localization achieved with the system. Adding multiple channels to the system also increases the complexity of the acoustic echo cancellation. Methods known from stereophonic systems extend to multichannel systems. By using arrays of microphones and loudspeakers it becomes possible to try to recreate a part of the acoustic wave field existing in the recording space. A method for achieving this is wave field synthesis (WFS). To solve the acoustic feedback problem, a 48 channel acoustic echo canceller was implemented. To maximize the achieved echo attenuation, a combination of adaptive and static filters were used. The implementation provided a stable solution that made normal conversation through the window possible. To verify the quality of the system, a listening test was performed. In the test, WFS was compared against three other recording and reproduction methods on four different attributes of the perceived sound scape. The results show that WFS offers clear potential to be used in multichannel communication systems and in creation of the acoustic opening

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