博碩士論文 102582003 詳細資訊




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姓名 李遠山(Yuan-Shan Lee)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 強健性音訊處理研究:從訊號增強到模型學習
(A Study on Robust Audio Processing: From Signal Enhancement to Model Learning)
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摘要(中) 強健性對於音訊辨識系統來說是非常關鍵的課題。本論文提出兩個方法做為前端(Front-end)處理,來去除干擾音對音訊辨識系統之影響。其一,針對環境噪音,本論文提出結合壓縮感測(Compressive Sensing, CS)之語音增強方法。我們利用時頻遮罩對有噪頻譜進行初步去噪,並且將遮罩後的剩餘頻譜視作不完整之觀測,引入壓縮感測技術來估測頻譜中遺失之資訊,以強化增強訊號的品質。更進一步地,我們也推導出最佳增益值,來去除頻譜重建過程中可能產生之噪音成份。其二,針對深度干擾音源,本論文提出基於複數深層遞迴神經網路(Complex-valued Recurrent Neural Network, C-DRNN)之音源分離方法。相較於現有深層學習方法,本論文所提出的方法能夠直接對複數頻譜進行處理,這樣做的好處是能夠同時估測目標音源之能量與相位,藉此提升音源分離之效果與品質。此外,我們在深層網路架構中加入複數的遮罩層,具有使分離音源頻譜平滑的效果,而加入之複數鑑別項則能夠保留目標音源間之差異性。在後端(Back-end)辨識方面,本論文也提出兩個具不同特性的方法。其一,我們引入協同表示的概念,提出基於聯合核化字典學習(Joint Kernel Dictionary Learning, JKDL)之聲音事件辨識系統。藉由在目標函式中加入分類誤差項,能夠在學習字典的過程中同時訓練線性分類器,達到強化辨識能力並節省時間的效果。核化方法則能夠將訓練資料投射至高維特徵空間,進一步加強辨識效果。其二,考量到真實世界中類別的界定並不是那麼明確,也就是類別之間會有一些模糊地帶或是重疊。我們利用階層式狄氏程序混合模型(Hierarchical Dirichlet Process Mixture Model, HDPMM)共用成分的特性,提出音樂情緒標註與檢索系統,另外我們也考量到共用的特性可能會造成類別間的混淆,基於線性鑑別分析的概念,在系統中加入鑑別性因子,來提升分類之效果。
摘要(英) Robustness against noise is a critical characteristic of an audio recognition (AR) system. To develop a robust AR system, this dissertation proposes two front-end processing methods. To suppress the effects of background noise on target sound, a speech enhancement method that is based on compressive sensing (CS) is proposed. A quasi-SNR criterion are first utilized to determine whether a frequency bin in the spectrogram is reliable, and a corresponding mask is designed. The mask-extracted components of spectra are regarded as partial observation. The CS theory is used to reconstruct components that are missing from partial observations. The noise component can be further removed by multiplying the imputed spectrum with the optimized gain. To separate the target sound from the interference, a source separation method that is based on a complex-valued deep recurrent neural network (C-DRNN) is developed. A key aspect of the C-DRNN is that the activations and weights are complex-valued. Phase estimation is integrated into the C-DRNN by the construction of a deep and complex-valued regression model in the time-frequency domain. This dissertation also develops two novel methods for back-end recognition. The first is a joint kernel dictionary learning (JKDL) method for sound event classification. Our JKDL learns the collaborative representation instead of the sparse representation. The learned representation is thus ``denser′′ than the sparse representation that is learned by K-SVD. Moreover, the discriminative ability is improved by adding a classification error term into the objective function. The second is a hierarchical Dirichlet process mixture model (HPDMM), whose components can be shared between models of each audio category. Therefore, the proposed emotion models provide a better capture of the relationship between real-world emotional states.
關鍵字(中) ★ 壓縮感測
★ 深層遞迴神經網路
★ 聯合字典學習
★ 階層式狄氏程序
關鍵字(英) ★ Compressive Sensing
★ Recurrent Neural Network
★ Joint Dictionary Learning
★ Dirichlet Process
論文目次 摘要 xi
Abstract xiii
1 Introduction 1
1.1 Motivation1
1.2 Speech Enhancement3
1.3 Source Separation 4
1.4 Sound Event Recognition 5
1.5 Music Emotion Recognition7
1.6 Organization of This Dissertation 8
2 Background 9
2.1 Compressive Sensing 9
2.2 Phase-incorporated Approaches 12
2.3 Collaborative Representation 15
2.4 Class-dependent Models 17
3 Compressive Sensing-Based Speech Enhancement 19
3.1 Proposed Method 20
3.1.1 Constructing an Overcomplete Dictionary 21
3.1.2 Missing Data Mask 22
3.1.3 Estimating Missing Data by CS 23
3.2 Experimental Results 30
3.2.1 Experimental Setting 30
3.2.2 Performance Metrics 31
3.2.3 Effects of Sparsity and Size of Training Dataset 31
3.2.4 Study of Ringing Artifacts from Imputation 33
3.2.5 Effects of Error Propagation 33
3.2.6 Baseline Algorithm 34
3.2.7 Experimental Results 35
4 Complex-valued Deep Neural Network for Phase-Incorporating Monaural
Source Separation 39
4.1 Complex-valued Gradients 40
4.2 Complex-valued Deep Neural Network (C-DNN) 42
4.2.1 Sparse Model Training 42
4.2.2 Complex-Valued Rectified Linear Unit 44
4.2.3 Error Back-propagation for C-DNN 44
4.3 Complex-valued Deep Recurrent Neural Network (C-DRNN) 46
4.3.1 Complex-Valued Recurrent Model 47
4.3.2 Complex-Valued Ratio Masking Layer 48
4.3.3 Incorporating Discriminative Constraint into Objective Function 50
4.3.4 Back-Propagation Through Time for C-DRNN 51
4.4 Experimental Results 54
4.4.1 Dataset and Evaluation Criteria 54
4.4.2 Baseline Methods 55
4.4.3 Experimental Settings 55
4.4.4 Comparison between SReLU and CReLU56
4.4.5 Effect of C-RM Layer57
4.4.6 Effect of Discriminative Training58
4.4.7 Comparing with Baseline Methods59
5 Sound Event Classification Using Joint Kernel Dictionary Learning 63
5.1 Joint Dictionary Learning (JDL) 64
5.1.1 Representation coding step64
5.1.2 Dictionary learning step 65
5.1.3 Classifier updating step 65
5.2 Joint Kernel Dictionary Learning (JKDL) 66
5.2.1 Representation coding step 66
5.2.2 Coefficient dictionary learning step 67
5.2.3 Classifier updating step 67
5.3 One-versus-One Classifier Extension68
5.4 Classification 68
5.5 Incremental JKDL 69
5.6 Experiments 70
5.6.1 Parameters Selection 73
5.6.2 Effect of Different Training Data Numbers 74
5.6.3 Effect of Different Dictionary Sizes76
5.6.4 Comparing the results in terms of precision, recall and F-score 77
5.6.5 Incremental JKDL 78
6 Hierarchical Dirichlet Process Mixture Model for Music Emotion Recognition 81
6.1 Proposed Method 82
6.1.1 Dirichlet Process Mixture Model 82
6.1.2 Hierarchical Dirichlet Process Mixture Model 84
6.1.3 Discriminant HDPMM 88
6.2 Experimental Results 95
6.2.1 Music Emotion Annotation 97
6.2.2 Music Emotion Retrieval 99
6.2.3 Music Emotion Retrieval 100
6.2.4 Discussion of the SR Method 101
7 Conclusion 103
Bibliography 107
A Publication List 121
A.1 Selected Journal Articles 121
A.2 Selected Conference Papers 122
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指導教授 王家慶(Jia-Ching Wang) 審核日期 2017-8-23
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