GLOTTAL EXCITATION EXTRACTION OF VOICED SPEECH - JOINTLY PARAMETRIC AND NONPARAMETRIC APPROACHES

The goal of this dissertation is to develop methods to recover glottal flow pulses, which contain biometrical information about the speaker. The excitation information estimated from an observed speech utterance is modeled as the source of an inverse problem. Windowed linear prediction analysis and inverse filtering are first used to deconvolve the speech signal to obtain a rough estimate of glottal flow pulses. Linear prediction and its inverse filtering can largely eliminate the vocal-tract response which is usually modeled as infinite impulse response filter. Some remaining vocal-tract components that reside in the estimate after inverse filtering are next removed by maximum-phase and minimum-phase decomposition which is implemented by applying the complex cepstrum to the initial estimate of the glottal pulses. The additive and residual errors from inverse filtering can be suppressed by higher-order statistics which is the method used to calculate cepstrum representations. Some features directly provided by the glottal source\u27s cepstrum representation as well as fitting parameters for estimated pulses are used to form feature patterns that were applied to a minimum-distance classifier to realize a speaker identification system with very limited subjects

Scalable image quality assessment with 2D mel-cepstrum and machine learning approach

Cataloged from PDF version of article.Measurement of image quality is of fundamental importance to numerous image and video processing applications. Objective image quality assessment (IQA) is a two-stage process comprising of the following: (a) extraction of important information and discarding the redundant one, (b) pooling the detected features using appropriate weights. These two stages are not easy to tackle due to the complex nature of the human visual system (HVS). In this paper, we first investigate image features based on two-dimensional (20) mel-cepstrum for the purpose of IQA. It is shown that these features are effective since they can represent the structural information, which is crucial for IQA. Moreover, they are also beneficial in a reduced-reference scenario where only partial reference image information is used for quality assessment. We address the second issue by exploiting machine learning. In our opinion, the well established methodology of machine learning/pattern recognition has not been adequately used for IQA so far; we believe that it will be an effective tool for feature pooling since the required weights/parameters can be determined in a more convincing way via training with the ground truth obtained according to subjective scores. This helps to overcome the limitations of the existing pooling methods, which tend to be over simplistic and lack theoretical justification. Therefore, we propose a new metric by formulating IQA as a pattern recognition problem. Extensive experiments conducted using six publicly available image databases (totally 3211 images with diverse distortions) and one video database (with 78 video sequences) demonstrate the effectiveness and efficiency of the proposed metric, in comparison with seven relevant existing metrics. (C) 2011 Elsevier Ltd. All rights reserved

Separation of Vocal and Non-Vocal Components from Audio Clip Using Correlated Repeated Mask (CRM)

Extraction of singing voice from music is one of the ongoing research topics in the field of speech recognition and audio analysis. In particular, this topic finds many applications in the music field, such as in determining music structure, lyrics recognition, and singer recognition. Although many studies have been conducted for the separation of voice from the background, there has been less study on singing voice in particular. In this study, efforts were made to design a new methodology to improve the separation of vocal and non-vocal components in audio clips using REPET [14]. In the newly designed method, we tried to rectify the issues encountered in the REPET method, while designing an improved repeating mask which is used to extract the non-vocal component in audio. The main reason why the REPET method was preferred over previous methods for this study is its independent nature. More specifically, the majority of existing methods for the separation of singing voice from music were constructed explicitly based on one or more assumptions

Bispectrum- and Bicoherence-Based Discriminative Features Used for Classification of Radar Targets and Atmospheric Formations

This chapter is dedicated to bispectrum-based signal processing in the surveillance radar applications. Detection, recognition, and classification of the targets by surveillance radars have various applications including security, military intelligence, battlefield purposes, boundary protection, as well as weather forecast. One of the particular and effective discriminative features commonly exploited in modern radar automatic target recognition (ATR) systems is the micro-Doppler (m-D) contributions extracted from joint time-frequency (TF) distribution. However, a common drawback of the energy-based strategy lies in the impossibility to retrieve additional particular information related to frequency-coupling and phase-coupling contributions containing in the radar backscattering. Phase coupling contains additional discriminative features related to individual target properties. Bispectrum-based strategy allows retrieving a phase-coupled data containing unique discriminative features related to individual target properties. Bispectrum tends to zero for a stationary zero-mean additive white Gaussian noise (AWGN), providing smoothing of AWGN in TF distributions. Hence, bispectrum-based approach allows improving extraction of robust discriminative features for ATR radar systems

불충분한 고장 데이터에 대한 딥러닝 기반 회전 기계 진단기술 학습방법 연구

학위논문(박사)--서울대학교 대학원 :공과대학 기계항공공학부,2020. 2. 윤병동.Deep Learning is a promising approach for fault diagnosis in mechanical applications. Deep learning techniques are capable of processing lots of data in once, and modelling them into desired diagnostic model. In industrial fields, however, we can acquire tons of data but barely useful including fault or failure data because failure in industrial fields is usually unacceptable. To cope with this insufficient fault data problem to train diagnostic model for rotating machinery, this thesis proposes three research thrusts: 1) filter-envelope blocks in convolution neural networks (CNNs) to incorporate the preprocessing steps for vibration signal; frequency filtering and envelope extraction for more optimal solution and reduced efforts in building diagnostic model, 2) cepstrum editing based data augmentation (CEDA) for diagnostic dataset consist of vibration signals from rotating machinery, and 3) selective parameter freezing (SPF) for efficient parameter transfer in transfer learning. The first research thrust proposes noble types of functional blocks for neural networks in order to learn robust feature to the vibration data. Conventional neural networks including convolution neural network (CNN), is tend to learn biased features when the training data is acquired from small cases of conditions. This can leads to unfavorable performance to the different conditions or other similar equipment. Therefore this research propose two neural network blocks which can be incorporated to the conventional neural networks and minimize the preprocessing steps, filter block and envelope block. Each block is designed to learn frequency filter and envelope extraction function respectively, in order to induce the neural network to learn more robust and generalized features from limited vibration samples. The second thrust presents a new data augmentation technique specialized for diagnostic data of vibration signals. Many data augmentation techniques exist for image data with no consideration for properties of vibration data. Conventional techniques for data augmentation, such as flipping, rotating, or shearing are not proper for 1-d vibration data can harm the natural property of vibration signal. To augment vibration data without losing the properties of its physics, the proposed method generate new samples by editing the cepstrum which can be done by adjusting the cepstrum component of interest. By doing reverse transform to the edited cepstrum, the new samples is obtained and this results augmented dataset which leads to higher accuracy for the diagnostic model. The third research thrust suggests a new parameter repurposing method for parameter transfer, which is used for transfer learning. The proposed SPF selectively freezes transferred parameters from source network and re-train only unnecessary parameters for target domain to reduce overfitting and preserve useful source features when the target data is limited to train diagnostic model.딥러닝은 기계 응용 분야의 결함 진단을 위한 유망한 접근 방식이다. 딥러닝 기술은 많은 양의 데이터를 학습하여 진단 모델의 개발을 용이하게 한다. 그러나 산업 분야에서는 많은 양의 데이터를 얻을 수 없거나 얻을 수 있더라도 고장 데이터는 일반적으로 획득하기 매우 어렵기 때문에 딥러닝 방법의 사용은 쉽지 않다. 회전 기계의 진단을 위하여 딥러닝을 학습시킬 때 발생하는 고장 데이터 부족 문제에 대처하기 위해 이 논문은 3 가지 연구를 제안한다. 1) 향상된 진동 특징 학습을 위한 필터-엔벨롭 네트워크 구조 2) 진동데이터 생성을 위한 Cepstrum 기반 데이터 증량법3) 전이 학습에서 효율적인 파라미터 전이를 위한 선택적 파라미터 동결법. 첫 번째 연구는 진동 데이터에 대한 강건한 특징을 배우기 위해 신경망에 대한 새로운 형태의 네트워크 블록들을 제안한다. 합성곱 신경망을 포함하는 종래의 신경망은 학습 데이터가 작은 경우에 데이터로부터 편향된 특징을 배우는 경향이 있으며, 이는 다른 조건에서 작동하는 경우나 다른 시스템에 대해 적용되었을 때 낮은 진단 성능을 보인다. 따라서 본 연구는 기존의 신경망에 함께 사용될 수 있는 필터 블록 및 엔벨롭 블록을 제안한다. 각 블록은 주파수 필터와 엔벨롭 추출 기능을 네트워크 내에서 스스로 학습하여 신경망이 제한된 학습 진동데이터로부터 보다 강건하고 일반화 된 특징을 학습하도록 한다. 두 번째 연구는 진동 신호의 진단 데이터에 특화된 새로운 데이터 증량법을 제안한다. 뒤집기, 회전 또는 전단과 같은 데이터 확대를 위한 이미지 데이터를 위한 기존의 기술이 1 차원 진동 데이터에 적합하지 않으며, 진동 신호의 물리적 특성에 맞지 않는 신호를 생성할 수 있다. 물리적 특성을 잃지 않고 진동 데이터를 증량하기 위해 제안된 방법은 cepstrum의 주요성분을 추출하고 조정하여 역 cepstrum을 수행하는 방식으로 새로운 샘플을 생성한다. 제안된 방법을 통해 데이터를 생성하여 증량돤 데이터세트는 진단 모델 학습에 대해 성능향상을 가져온다. 세 번째 연구는 전이 학습에 사용되는 파라미터 전이를 위한 새로운 파라미터 재학습법을 제안한다. 제안된 선택적 파라미터 동결법은 소스 네트워크에서 전이된 파라미터를 선택적으로 동결하고 대상 도메인에 대해 불필요한 파라미터만 재학습하여 대상 데이터가 진단 모델에 재학습될 때의 과적합을 줄이고 소스 네트워크의 성능을 보존한다. 제안된 세 방법은 독립적으로 또는 동시에 진단모델에 사용되어 부족한 고장데이터로 인한 진단성능의 감소를 경감하거나 더 높은 성능을 이끌어낼 수 있다.Chapter 1 Introduction 13 1.1 Motivation 13 1.2 Research Scope and Overview 15 1.3 Structure of the Thesis 19 Chapter 2 Literature Review 20 2.1 Deep Neural Networks 20 2.2 Transfer Learning and Parameter Transfer 23 Chapter 3 Description of Testbed Data 26 3.1 Bearing Data I: Case Western Reserve University Data 26 3.2 Bearing Data II: Accelerated Life Test Test-bed 27 Chapter 4 Filter-Envelope Blocks in Neural Network for Robust Feature Learning 32 4.1 Preliminary Study of Problems In Use of CNN for Vibration Signals 34 4.1.1 Class Confusion Problem of CNN Model to Different Conditions 34 4.1.2 Benefits of Frequency Filtering and Envelope Extraction for Fault Diagnosis in Vibration Signals 37 4.2 Proposed Network Block 1: Filter Block 41 4.2.1 Spectral Feature Learning in Neural Network 42 4.2.2 FIR Band-pass Filter in Neural Network 45 4.2.3 Result and Discussion 48 4.3 Proposed Neural Block 2: Envelope Block 48 4.3.1 Max-Average Pooling Block for Envelope Extraction 51 4.3.2 Adaptive Average Pooling for Learnable Envelope Extractor 52 4.3.3 Result and Discussion 54 4.4 Filter-Envelope Network for Fault Diagnosis 56 4.4.1 Combinations of Filter-Envelope Blocks for the use of Rolling Element Bearing Fault Diagnosis 56 4.4.2 Summary and Discussion 58 Chapter 5 Cepstrum Editing Based Data Augmentation for Vibration Signals 59 5.1 Brief Review of Data Augmentation for Deep Learning 59 5.1.1 Image Augmentation to Enlarge Training Dataset 59 5.1.2 Data Augmentation for Vibration Signal 61 5.2 Cepstrum Editing based Data Augmentation 62 5.2.1 Cepstrum Editing as a Signal Preprocessing 62 5.2.2 Cepstrum Editing based Data Augmentation 64 5.3 Results and Discussion 65 5.3.1 Performance validation to rolling element bearing diagnosis 65 Chapter 6 Selective Parameter Freezing for Parameter Transfer with Small Dataset 71 6.1 Overall Procedure of Selective Parameter Freezing 72 6.2 Determination Sensitivity of Source Network Parameters 75 6.3 Case Study 1: Transfer to Different Fault Size 76 6.3.1 Performance by hyperparameter α 77 6.3.2 Effect of the number of training samples and network size 79 6.4 Case Study 2: Transfer from Artificial to Natural Fault 81 6.4.1 Diagnostic performance for proposed method 82 6.4.2 Visualization of frozen parameters by hyperparameter α 83 6.4.3 Visual inspection of feature space 85 6.5 Conclusion 87 Chapter 7 91 7.1 Contributions and Significance 91Docto

Automated Accident Detection In Intersections Via Digital Audio Signal Processing

The aim of this thesis is to design a system for automated accident detection in intersections. The input to the system is a three-second audio signal. The system can be operated in two modes: two-class and multi-class. The output of the two-class system is a label of ?crash? or ?non-crash?. In the multi-class system, the output is the label of ?crash? or various non-crash incidents including ?pile drive?, ?brake?, and ?normal-traffic? sounds. The system designed has three main steps in processing the input audio signal. They are: feature extraction, feature optimization and classification. Five different methods of feature extraction are investigated and compared; they are based on the discrete wavelet transform, fast Fourier transform, discrete cosine transform, real cepstrum transform and Mel frequency cepstral transform. Linear discriminant analysis (LDA) is used to optimize the features obtained in the feature extraction stage by linearly combining the features using different weights. Three types of statistical classifiers are investigated and compared: the nearest neighbor, nearest mean, and maximum likelihood methods. Data collected from Jackson, MS and Starkville, MS and the crash signals obtained from Texas Transportation Institute crash test facility are used to train and test the designed system. The results showed that the wavelet based feature extraction method with LDA and maximum likelihood classifier is the optimum design. This wavelet-based system is computationally inexpensive compared to other methods. The system produced classification accuracies of 95% to 100% when the input signal has a signal-to-noise-ratio of at least 0 decibels. These results show that the system is capable of effectively classifying ?crash? or ?non-crash? on a given input audio signal