本論文結合麥克風陣列訊號處理與深度神經網路技術，應用於語音增強及遠距音訊重現，使其能夠適用於各種聲學環境和不同的陣列配置。主要研究涵蓋三大重要主題: 盲語音分離、個人化語音增強和基於麥克風陣列之雙耳聲場重現技術。盲語音分離之目的是僅通過麥克風觀測訊號來提取每位說話者的訊號。為此，本論文提出一個基於空間-時間活動之語者自動分段和分離演算法，此方法結合數位訊號處理前處理和深度神經網路後端，從而提升語音品質和清晰度。個人化語音增強之主要目的是為抑制非目標語音的干擾，如非目標語者之語音訊號和電視噪音。為使個人化語音增強技術能夠廣泛適用於各式陣列收音裝置，本論文提出不限於特定陣列配置和拓樸架構之與陣列配置無關個人化語音增強演算法。遠距雙耳音訊重現為本論文的另一大重點，基於麥克風陣列之雙耳聲場重現技術旨在將麥克風陣列捕捉到之遠端聲學場景轉換成雙耳訊號，以便在近端透過耳機重現遠端聲場。為此，本論文提出一套與陣列配置無關之創新遠距雙耳音訊重現系統，使其適用於各種不同的麥克風陣列配置。此外，本論文亦引入一個前瞻概念，允許使用者在語音增強及背景環境音保留模式之間自由切換，此設計使提出系統能夠靈活應用於各種不同的遠距音訊重現場景。

This dissertation integrates Microphone Array Signal Processing (MASP) and Deep Neural Network (DNN) to Speech Enhancement (SE) and Audio Telepresence (AT) problems in various acoustic environments and different array configurations. Three main topics are covered: Blind Speech Separation (BSS), Personalized Speech Enhancement (PSE), and array-based binaural rendering. Blind speech separation aims to extract the signal of each speaker using only microphone signals. This dissertation proposes a Spatial-Temporal Activity-Informed Diarization and Separation (STAIDS) algorithm based on a Digital Signal Processing (DSP)-based feature extraction front-end and a DNN-based diarization and separation back-end, which leads to improved speech quality and intelligibility. PSE aims to suppress speech-like interferences such as competing speakers and TV noise. The proposed PSE system is “array agnostic,” meaning that it does not rely on specific array configurations and topologies. Binaural AT (BAT) is another focus of this dissertation, where BAT seeks to transport the far-end acoustic scene captured by a microphone array into binaural signals for headphone reproduction at the near-end. A novel BAT system is proposed that can be adapted to different array setups without reconfiguration. Finally, a “scalable” BAT system is proposed to migrate between two extremes of ambience preservation and signal enhancement to address a full range of AT application scenarios.

致謝 i
摘要 ii
Abstract iii
TABLE OF CONTENTS iv
LIST OF TABLES vii
LIST OF FIGURES x
CHAPTER 1. INTRODUCTION 1
CHAPTER 2. BLIND SPEECH SEPARATION 16
2.1 Problem formulation and signal model 17
2.2 Baseline method: the simplex-based method 18
2.2.1 Spatial feature extraction 19
2.2.2 Speaker diarization 20
2.2.3 Speaker separation 22
2.3 Proposed STAIDS system 25
2.3.1 Spatial feature extraction 26
2.3.2 Speaker diarization 27
2.3.3 Speaker separation 32
2.4 Experimental study 40
2.4.1 Data preparation 40
2.4.2 Implementation details and evaluation metrics 43
2.4.3 Spatial feature robustness 44
2.4.4 Speaker counting performance 47
2.4.5 Speaker diarization performance 50
2.4.6 Speaker separation performance 52
2.5 Summary 56
CHAPTER 3. PERSONALIZED SPEECH ENHANCEMENT 57
3.1 Problem formulation and signal model 58
3.2 Proposed ARCA-PSE system 59
3.2.1 The LSTSC feature 60
3.2.2 Speaker encoder 69
3.2.3 Target speech sifting network 69
3.3 Experimental study 72
3.3.1 Data preparation 72
3.3.2 Training and validation dataset 73
3.3.3 Test set 74
3.3.4 Baselines and implementation details 75
3.3.5 Evaluation metrics 77
3.3.6 Spatial feature robustness 78
3.3.7 Performance with different array configurations 81
3.3.8 Performance with different number of microphones 85
3.4 Summary 87
CHAPTER 4. ARRAY-BASED BINAURAL RENDERING 89
4.1 Problem formulation and signal model 90
4.2 Proposed ACIS-BAT system 92
4.2.1 The SCORE module 93
4.2.2 The BRnet 95
4.2.3 Training procedure and loss function 97
4.2.4 Training the model for both SE and AP tasks 98
4.3 Experimental study 99
4.3.1 Data preparation 100
4.3.2 Baselines and implementation details 104
4.3.3 Spatial feature robustness 105
4.3.4 Objective performance 108
4.3.5 Scalability 111
4.3.6 Subjective performance 113
4.4 Summary 116
CHAPTER 5. CONCLUSIONS 117
CHAPTER 6. FUTURE PERSPECTIVES 120
6.1 Online blind speech separation 120
6.2 Computationally efficient personalized speech enhancement 121
6.3 Global audio telepresence 121
ABBREVIATIONS 123
REFERENCES 127
PUBLICATIONS AND PATENT 147

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