9 research outputs found
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
Dirty RF Signal Processing for Mitigation of Receiver Front-end Non-linearity
Moderne drahtlose Kommunikationssysteme stellen hohe und teilweise
gegensätzliche Anforderungen an die Hardware der Funkmodule, wie z.B.
niedriger Energieverbrauch, große Bandbreite und hohe Linearität. Die
Gewährleistung einer ausreichenden Linearität ist, neben anderen analogen
Parametern, eine Herausforderung im praktischen Design der Funkmodule. Der
Fokus der Dissertation liegt auf breitbandigen HF-Frontends für
Software-konfigurierbare Funkmodule, die seit einigen Jahren kommerziell
verfügbar sind. Die praktischen Herausforderungen und Grenzen solcher
flexiblen Funkmodule offenbaren sich vor allem im realen Experiment. Eines
der Hauptprobleme ist die Sicherstellung einer ausreichenden analogen
Performanz über einen weiten Frequenzbereich. Aus einer Vielzahl an
analogen Störeffekten behandelt die Arbeit die Analyse und Minderung von
Nichtlinearitäten in Empfängern mit direkt-umsetzender Architektur. Im
Vordergrund stehen dabei Signalverarbeitungsstrategien zur Minderung
nichtlinear verursachter Interferenz - ein Algorithmus, der besser unter
"Dirty RF"-Techniken bekannt ist. Ein digitales Verfahren nach der
Vorwärtskopplung wird durch intensive Simulationen, Messungen und
Implementierung in realer Hardware verifiziert. Um die Lücken zwischen
Theorie und praktischer Anwendbarkeit zu schließen und das Verfahren in
reale Funkmodule zu integrieren, werden verschiedene Untersuchungen
durchgeführt. Hierzu wird ein erweitertes Verhaltensmodell entwickelt, das
die Struktur direkt-umsetzender Empfänger am besten nachbildet und damit
alle Verzerrungen im HF- und Basisband erfasst. Darüber hinaus wird die
Leistungsfähigkeit des Algorithmus unter realen Funkkanal-Bedingungen
untersucht. Zusätzlich folgt die Vorstellung einer ressourceneffizienten
Echtzeit-Implementierung des Verfahrens auf einem FPGA. Abschließend
diskutiert die Arbeit verschiedene Anwendungsfelder, darunter spektrales
Sensing, robuster GSM-Empfang und GSM-basiertes Passivradar. Es wird
gezeigt, dass nichtlineare Verzerrungen erfolgreich in der digitalen
Domäne gemindert werden können, wodurch die Bitfehlerrate gestörter
modulierter Signale sinkt und der Anteil nichtlinear verursachter
Interferenz minimiert wird. Schließlich kann durch das Verfahren die
effektive Linearität des HF-Frontends stark erhöht werden. Damit wird der
zuverlässige Betrieb eines einfachen Funkmoduls unter dem Einfluss der
Empfängernichtlinearität möglich. Aufgrund des flexiblen Designs ist der
Algorithmus für breitbandige Empfänger universal einsetzbar und ist nicht
auf Software-konfigurierbare Funkmodule beschränkt.Today's wireless communication systems place high requirements on the
radio's hardware that are largely mutually exclusive, such as low power
consumption, wide bandwidth, and high linearity. Achieving a sufficient
linearity, among other analogue characteristics, is a challenging issue in
practical transceiver design. The focus of this thesis is on wideband
receiver RF front-ends for software defined radio technology, which became
commercially available in the recent years. Practical challenges and
limitations are being revealed in real-world experiments with these radios.
One of the main problems is to ensure a sufficient RF performance of the
front-end over a wide bandwidth. The thesis covers the analysis and
mitigation of receiver non-linearity of typical direct-conversion receiver
architectures, among other RF impairments. The main focus is on DSP-based
algorithms for mitigating non-linearly induced interference, an approach
also known as "Dirty RF" signal processing techniques. The conceived
digital feedforward mitigation algorithm is verified through extensive
simulations, RF measurements, and implementation in real hardware. Various
studies are carried out that bridge the gap between theory and practical
applicability of this approach, especially with the aim of integrating that
technique into real devices. To this end, an advanced baseband behavioural
model is developed that matches to direct-conversion receiver architectures
as close as possible, and thus considers all generated distortions at RF
and baseband. In addition, the algorithm's performance is verified under
challenging fading conditions. Moreover, the thesis presents a
resource-efficient real-time implementation of the proposed solution on an
FPGA. Finally, different use cases are covered in the thesis that includes
spectrum monitoring or sensing, GSM downlink reception, and GSM-based
passive radar. It is shown that non-linear distortions can be successfully
mitigated at system level in the digital domain, thereby decreasing the bit
error rate of distorted modulated signals and reducing the amount of
non-linearly induced interference. Finally, the effective linearity of the
front-end is increased substantially. Thus, the proper operation of a
low-cost radio under presence of receiver non-linearity is possible. Due to
the flexible design, the algorithm is generally applicable for wideband
receivers and is not restricted to software defined radios
Development of Novel Techniques to Study Nonlinear Active Noise Control
Active noise control has been a field of growing interest over the past few decades. The challenges thrown by active noise control have attracted the notice of the
scientific community to engage them in intense level of research. Cancellation of acoustic noise electronically in a simple and efficient way is the vital merit of the active noise control system. A detailed study about existing strategies for active noise control has been undertaken in the present work. This study has given an insight regarding various factors influencing performance of modern active noise control systems. The development of new training algorithms and structures for active noise control are active fields of research which are exploiting the benefits of different signal processing
and soft- computing techniques. The nonlinearity contributed by environment and various components of active noise control system greatly affects the ultimate
performance of an active noise canceller. This fact motivated to pursue the research work in developing novel architectures and algorithms to address the issues of nonlinear active noise control. One of the primary focus of the work is the application of artificial neural network to effectively combat the problem of active noise control. This is because artificial neural networks are inherently nonlinear processors and possesses capabilities of universal approximation and thus are well suited to exhibit high performance when used in nonlinear active noise control. The present work contributed significantly in designing efficient nonlinear active noise canceller based on neural network platform. Novel neural filtered-x least mean square and neural filtered-e least mean square algorithms are proposed for nonlinear active noise control taking into consideration the
nonlinear secondary path. Employing Legendre neural network led the development of a set new adaptive algorithms such as Legendre filtered-x least mean square, Legendre vi filtered-e least mean square, Legendre filtered-x recursive least square and fast Legendre filtered-x least mean square algorithms. The proposed algorithms outperformed the existing standard algorithms for nonlinear active noise control in terms of steady state mean square error with reduced computational complexity. Efficient frequency domain implementation of some the proposed algorithms have been undertaken to exploit its benefits. Exhaustive simulation studies carried out have established the efficacy of the proposed architectures and algorithms