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

Dynamic diffuse signal processing for sound reinforcement and reproduction.

High inter-channel coherence between signals emitted from multiple loudspeakers can cause undesirable acoustic and psychoacoustic effects. Examples include position-dependent low-frequency magnitude response variation, where comb-filtering leads to the attenuation of certain frequencies dependent on path length differences between multiple coherent sources, lack of apparent source width in multi-channel reproduction and lack of externalization in headphone reproduction. This work examines a time-variant, real-time decorrelation algorithm for the reduction of coherence between sources as well as between direct sound and early reflections, with a focus on minimization of low-frequency magnitude response variation. The algorithm is applicable to a wide range of sound reinforcement and reproduction applications, including those requiring full-band decorrelation. Key variables which control the balance between decorrelation and processing artifacts such as transient smearing are described and evaluated using a MUSHRA test. Variable values which render the processing transparent whilst still providing decorrelation are discussed. Additionally, the benefit of transient preservation is investigated and is shown to increase transparency.N/

Signal decorrelation for sound reinforcement system crossovers

While sound reinforcement technology has progressed significantly in recent decades, aspects of system deployment remain largely unchanged, principally the use of stereo (or multiple mono) left/right configurations and crossover networks. As such, the issue of coherent interference between overlapping spatial and spectral coverage remains a challenge to system engineers. This paper focuses on the application of a perceptually transparent method of decorrelation, known as diffuse signal processing (DiSP), to minimize coherent interference within key elements of sound systems. Experiments were conducted with scale model loudspeakers in a hemi-anechoic chamber, mounting the systems onto an automated turntable to inspect the effectiveness of decorrelation over a wide polar range. Results indicate that the application of decorrelation has the potential to significantly reduce spatial variance across an audience area, although further work is necessary to optimize the decorrelation filters to improve performance consistency

Investigations into the Perception of Vertical Interchannel Decorrelation in 3D Surround Sound Reproduction

The use of three-dimensional (3D) surround sound systems has seen a rapid increase over recent years. In two-dimensional (2D) loudspeaker formats (i.e. two-channel stereophony (stereo) and 5.1 Surround), horizontal interchannel decorrelation is a well-established technique for controlling the horizontal spread of a phantom image. Use of interchannel decorrelation can also be found within established two-to-five channel upmixing methods (stereo to 5.1). More recently, proprietary algorithms have been developed that perform 2D-to-3D upmixing, which presumably make use of interchannel decorrelation as well; however, it is not currently known how interchannel decorrelation is perceived in the vertical domain. From this, it is considered that formal investigations into the perception of vertical interchannel decorrelation are necessary. Findings from such experiments may contribute to the improved control of a sound source within 3D surround systems (i.e. the vertical spread), in addition to aiding the optimisation of 2D-to-3D upmixing algorithms. The current thesis presents a series of experiments that systematically assess vertical interchannel decorrelation under various conditions. Firstly, a comparison is made between horizontal and vertical interchannel decorrelation, where it is found that vertical decorrelation is weaker than horizontal decorrelation. However, it is also seen that vertical decorrelation can generate a significant increase of vertical image spread (VIS) for some conditions. Following this, vertical decorrelation is assessed for octave-band pink noise stimuli at various azimuth angles to the listener. The results demonstrate that vertical decorrelation is dependent on both frequency and presentation angle – a general relationship between the interchannel cross-correlation (ICC) and VIS is observed for the 500 Hz octave-band and above, and strongest for the 8 kHz octave-band. Objective analysis of these stimuli signals determined that spectral changes at higher frequencies appear to be associated with VIS perception – at 0° azimuth, the 8 and 16 kHz octave-bands demonstrate potential spectral cues, at ±30°, similar cues are seen in the 4, 8 and 16 kHz bands, and from ±110°, cues are featured in the 2, 4, 8 and 16 kHz bands. In the case of the 8 kHz octave-band, it seems that vertical decorrelation causes a ‘filling in’ of vertical localisation notch cues, potentially resulting in ambiguous perception of vertical extent. In contrast, the objective analysis suggests that VIS perception of the 500 Hz and 1 kHz bands may have been related to early reflections in the listening room. From the experiments above, it is demonstrated that the perception of VIS from vertical interchannel decorrelation is frequency-dependent, with high frequencies playing a particularly important role. A following experiment explores the vertical decorrelation of high frequencies only, where it is seen that decorrelation of the 500 Hz octave-band and above produces a similar perception of VIS to broadband decorrelation, whilst improving tonal quality. The results also indicate that decorrelation of the 8 kHz octave-band and above alone can significantly increase VIS, provided the source signal has sufficient high frequency energy. The final experimental chapter of the present thesis aims to provide a controlled assessment of 2D-to-3D upmixing, taking into account the findings of the previous experiments. In general, 2D-to-3D upmixing by vertical interchannel decorrelation had little impact on listener envelopment (LEV), when compared against a level-matched 2D 5.1 reference. Furthermore, amplitude-based decorrelation appeared to be marginally more effective, and ‘high-pass decorrelation’ resulted in slightly better tonal quality for sources that featured greater low frequency energy

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