On the Impact of Voice Anonymization on Speech-Based COVID-19 Detection

With advances seen in deep learning, voice-based applications are burgeoning, ranging from personal assistants, affective computing, to remote disease diagnostics. As the voice contains both linguistic and paralinguistic information (e.g., vocal pitch, intonation, speech rate, loudness), there is growing interest in voice anonymization to preserve speaker privacy and identity. Voice privacy challenges have emerged over the last few years and focus has been placed on removing speaker identity while keeping linguistic content intact. For affective computing and disease monitoring applications, however, the paralinguistic content may be more critical. Unfortunately, the effects that anonymization may have on these systems are still largely unknown. In this paper, we fill this gap and focus on one particular health monitoring application: speech-based COVID-19 diagnosis. We test two popular anonymization methods and their impact on five different state-of-the-art COVID-19 diagnostic systems using three public datasets. We validate the effectiveness of the anonymization methods, compare their computational complexity, and quantify the impact across different testing scenarios for both within- and across-dataset conditions. Lastly, we show the benefits of anonymization as a data augmentation tool to help recover some of the COVID-19 diagnostic accuracy loss seen with anonymized data.Comment: 11 pages, 10 figure

A Compact CNN-Based Speech Enhancement With Adaptive Filter Design Using Gabor Function And Region-Aware Convolution

Speech enhancement (SE) is used in many applications, such as hearing devices, to improve speech intelligibility and quality. Convolutional neural network-based (CNN-based) SE algorithms in literature often employ generic convolutional filters that are not optimized for SE applications. This paper presents a CNN-based SE algorithm with an adaptive filter design (named ‘CNN-AFD’) using Gabor function and region-aware convolution. The proposed algorithm incorporates fixed Gabor functions into convolutional filters to model human auditory processing for improved denoising performance. The feature maps obtained from the Gabor-incorporated convolutional layers serve as learnable guided masks (tuned at backpropagation) for generating adaptive custom region-aware filters. The custom filters extract features from speech regions (i.e., ‘region-aware’) while maintaining translation-invariance. To reduce the high cost of inference of the CNN, skip convolution and activation analysis-wise pruning are explored. Employing skip convolution allowed the training time per epoch to be reduced by close to 40%. Pruning of neurons with high numbers of zero activations complements skip convolution and significantly reduces model parameters by more than 30%. The proposed CNN-AFD outperformed all four CNN-based SE baseline algorithms (i.e., a CNN-based SE employing generic filters, a CNN-based SE without region-aware convolution, a CNN-based SE trained with complex spectrograms and a CNN-based SE processing in the time-domain) with an average of 0.95, 1.82 and 0.82 in short-time objective intelligibility (STOI), perceptual evaluation of speech quality (PESQ) and logarithmic spectral distance (LSD) scores, respectively, when tasked to denoise speech contaminated with NOISEX-92 noises at −5, 0 and 5 dB signal-to-noise ratios (SNRs)

Automatic Speech Recognition System to Analyze Autism Spectrum Disorder in Young Children

It's possible to learn things about a person just by listening to their voice. When trying to construct an abstract concept of a speaker, it is essential to extract significant features from audio signals that are modulation-insensitive. This research assessed how individuals with autism spectrum disorder (ASD) recognize and recall voice identity. Autism spectrum disorder is the abbreviation for autism spectrum disorder. Both the ASD group and the control group performed equally well in a task in which they were asked to choose the name of a newly-learned speaker based on his or her voice. However, the ASD group outperformed the control group in a subsequent familiarity test in which they were asked to differentiate between previously trained voices and untrained voices. Persons with ASD classified voices numerically according to the exact acoustic characteristics, whereas non - autistic individuals classified voices qualitatively depending on the acoustic patterns associated to the speakers' physical and psychological traits. Child vocalizations show potential as an objective marker of developmental problems like Autism. In typical detection systems, hand-crafted acoustic features are input into a discriminative classifier, but its accuracy and resilience are limited by the number of its training data. This research addresses using CNN-learned feature representations to classify children's speech with developmental problems. On the Child Pathological and Emotional Speech database, we compare several acoustic feature sets. CNN-based approaches perform comparably to conventional paradigms in terms of unweighted average recall

USING DEEP LEARNING-BASED FRAMEWORK FOR CHILD SPEECH EMOTION RECOGNITION

Biological languages of the body through which human emotion can be detected abound including heart rate, facial expressions, movement of the eyelids and dilation of the eyes, body postures, skin conductance, and even the speech we make. Speech emotion recognition research started some three decades ago, and the popular Interspeech Emotion Challenge has helped to propagate this research area. However, most speech recognition research is focused on adults and there is very little research on child speech. This dissertation is a description of the development and evaluation of a child speech emotion recognition framework. The higher-level components of the framework are designed to sort and separate speech based on the speaker’s age, ensuring that focus is only on speeches made by children. The framework uses Baddeley’s Theory of Working Memory to model a Working Memory Recurrent Network that can process and recognize emotions from speech. Baddeley’s Theory of Working Memory offers one of the best explanations on how the human brain holds and manipulates temporary information which is very crucial in the development of neural networks that learns effectively. Experiments were designed and performed to provide answers to the research questions, evaluate the proposed framework, and benchmark the performance of the framework with other methods. Satisfactory results were obtained from the experiments and in many cases, our framework was able to outperform other popular approaches. This study has implications for various applications of child speech emotion recognition such as child abuse detection and child learning robots

Apprentissage automatique pour le codage cognitif de la parole

Depuis les années 80, les codecs vocaux reposent sur des stratégies de codage à court terme qui fonctionnent au niveau de la sous-trame ou de la trame (généralement 5 à 20 ms). Les chercheurs ont essentiellement ajusté et combiné un nombre limité de technologies disponibles (transformation, prédiction linéaire, quantification) et de stratégies (suivi de forme d'onde, mise en forme du bruit) pour construire des architectures de codage de plus en plus complexes. Dans cette thèse, plutôt que de s'appuyer sur des stratégies de codage à court terme, nous développons un cadre alternatif pour la compression de la parole en codant les attributs de la parole qui sont des caractéristiques perceptuellement importantes des signaux vocaux. Afin d'atteindre cet objectif, nous résolvons trois problèmes de complexité croissante, à savoir la classification, la prédiction et l'apprentissage des représentations. La classification est un élément courant dans les conceptions de codecs modernes. Dans un premier temps, nous concevons un classifieur pour identifier les émotions, qui sont parmi les attributs à long terme les plus complexes de la parole. Dans une deuxième étape, nous concevons un prédicteur d'échantillon de parole, qui est un autre élément commun dans les conceptions de codecs modernes, pour mettre en évidence les avantages du traitement du signal de parole à long terme et non linéaire. Ensuite, nous explorons les variables latentes, un espace de représentations de la parole, pour coder les attributs de la parole à court et à long terme. Enfin, nous proposons un réseau décodeur pour synthétiser les signaux de parole à partir de ces représentations, ce qui constitue notre dernière étape vers la construction d'une méthode complète de compression de la parole basée sur l'apprentissage automatique de bout en bout. Bien que chaque étape de développement proposée dans cette thèse puisse faire partie d'un codec à elle seule, chaque étape fournit également des informations et une base pour la prochaine étape de développement jusqu'à ce qu'un codec entièrement basé sur l'apprentissage automatique soit atteint. Les deux premières étapes, la classification et la prédiction, fournissent de nouveaux outils qui pourraient remplacer et améliorer des éléments des codecs existants. Dans la première étape, nous utilisons une combinaison de modèle source-filtre et de machine à état liquide (LSM), pour démontrer que les caractéristiques liées aux émotions peuvent être facilement extraites et classées à l'aide d'un simple classificateur. Dans la deuxième étape, un seul réseau de bout en bout utilisant une longue mémoire à court terme (LSTM) est utilisé pour produire des trames vocales avec une qualité subjective élevée pour les applications de masquage de perte de paquets (PLC). Dans les dernières étapes, nous nous appuyons sur les résultats des étapes précédentes pour concevoir un codec entièrement basé sur l'apprentissage automatique. un réseau d'encodage, formulé à l'aide d'un réseau neuronal profond (DNN) et entraîné sur plusieurs bases de données publiques, extrait et encode les représentations de la parole en utilisant la prédiction dans un espace latent. Une approche d'apprentissage non supervisé basée sur plusieurs principes de cognition est proposée pour extraire des représentations à partir de trames de parole courtes et longues en utilisant l'information mutuelle et la perte contrastive. La capacité de ces représentations apprises à capturer divers attributs de la parole à court et à long terme est démontrée. Enfin, une structure de décodage est proposée pour synthétiser des signaux de parole à partir de ces représentations. L'entraînement contradictoire est utilisé comme une approximation des mesures subjectives de la qualité de la parole afin de synthétiser des échantillons de parole à consonance naturelle. La haute qualité perceptuelle de la parole synthétisée ainsi obtenue prouve que les représentations extraites sont efficaces pour préserver toutes sortes d'attributs de la parole et donc qu'une méthode de compression complète est démontrée avec l'approche proposée.Abstract: Since the 80s, speech codecs have relied on short-term coding strategies that operate at the subframe or frame level (typically 5 to 20ms). Researchers essentially adjusted and combined a limited number of available technologies (transform, linear prediction, quantization) and strategies (waveform matching, noise shaping) to build increasingly complex coding architectures. In this thesis, rather than relying on short-term coding strategies, we develop an alternative framework for speech compression by encoding speech attributes that are perceptually important characteristics of speech signals. In order to achieve this objective, we solve three problems of increasing complexity, namely classification, prediction and representation learning. Classification is a common element in modern codec designs. In a first step, we design a classifier to identify emotions, which are among the most complex long-term speech attributes. In a second step, we design a speech sample predictor, which is another common element in modern codec designs, to highlight the benefits of long-term and non-linear speech signal processing. Then, we explore latent variables, a space of speech representations, to encode both short-term and long-term speech attributes. Lastly, we propose a decoder network to synthesize speech signals from these representations, which constitutes our final step towards building a complete, end-to-end machine-learning based speech compression method. The first two steps, classification and prediction, provide new tools that could replace and improve elements of existing codecs. In the first step, we use a combination of source-filter model and liquid state machine (LSM), to demonstrate that features related to emotions can be easily extracted and classified using a simple classifier. In the second step, a single end-to-end network using long short-term memory (LSTM) is shown to produce speech frames with high subjective quality for packet loss concealment (PLC) applications. In the last steps, we build upon the results of previous steps to design a fully machine learning-based codec. An encoder network, formulated using a deep neural network (DNN) and trained on multiple public databases, extracts and encodes speech representations using prediction in a latent space. An unsupervised learning approach based on several principles of cognition is proposed to extract representations from both short and long frames of data using mutual information and contrastive loss. The ability of these learned representations to capture various short- and long-term speech attributes is demonstrated. Finally, a decoder structure is proposed to synthesize speech signals from these representations. Adversarial training is used as an approximation to subjective speech quality measures in order to synthesize natural-sounding speech samples. The high perceptual quality of synthesized speech thus achieved proves that the extracted representations are efficient at preserving all sorts of speech attributes and therefore that a complete compression method is demonstrated with the proposed approach

Ubiquitous Technologies for Emotion Recognition

Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now, with the advent of wearable, mobile, and ubiquitous technologies, that we can aim to sense and recognize emotions, continuously and in real time. This book brings together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and the recognition of human emotions

A Review of Deep Learning Techniques for Speech Processing

The field of speech processing has undergone a transformative shift with the advent of deep learning. The use of multiple processing layers has enabled the creation of models capable of extracting intricate features from speech data. This development has paved the way for unparalleled advancements in speech recognition, text-to-speech synthesis, automatic speech recognition, and emotion recognition, propelling the performance of these tasks to unprecedented heights. The power of deep learning techniques has opened up new avenues for research and innovation in the field of speech processing, with far-reaching implications for a range of industries and applications. This review paper provides a comprehensive overview of the key deep learning models and their applications in speech-processing tasks. We begin by tracing the evolution of speech processing research, from early approaches, such as MFCC and HMM, to more recent advances in deep learning architectures, such as CNNs, RNNs, transformers, conformers, and diffusion models. We categorize the approaches and compare their strengths and weaknesses for solving speech-processing tasks. Furthermore, we extensively cover various speech-processing tasks, datasets, and benchmarks used in the literature and describe how different deep-learning networks have been utilized to tackle these tasks. Additionally, we discuss the challenges and future directions of deep learning in speech processing, including the need for more parameter-efficient, interpretable models and the potential of deep learning for multimodal speech processing. By examining the field's evolution, comparing and contrasting different approaches, and highlighting future directions and challenges, we hope to inspire further research in this exciting and rapidly advancing field

Deep spiking neural networks with applications to human gesture recognition

The spiking neural networks (SNNs), as the 3rd generation of Artificial Neural Networks (ANNs), are a class of event-driven neuromorphic algorithms that potentially have a wide range of application domains and are applicable to a variety of extremely low power neuromorphic hardware. The work presented in this thesis addresses the challenges of human gesture recognition using novel SNN algorithms. It discusses the design of these algorithms for both visual and auditory domain human gesture recognition as well as event-based pre-processing toolkits for audio signals. From the visual gesture recognition aspect, a novel SNN-based event-driven hand gesture recognition system is proposed. This system is shown to be effective in an experiment on hand gesture recognition with its spiking recurrent convolutional neural network (SCRNN) design, which combines both designed convolution operation and recurrent connectivity to maintain spatial and temporal relations with address-event-representation (AER) data. The proposed SCRNN architecture can achieve arbitrary temporal resolution, which means it can exploit temporal correlations between event collections. This design utilises a backpropagation-based training algorithm and does not suffer from gradient vanishing/explosion problems. From the audio perspective, a novel end-to-end spiking speech emotion recognition system (SER) is proposed. This system employs the MFCC as its main speech feature extractor as well as a self-designed latency coding algorithm to effciently convert the raw signal to AER input that can be used for SNN. A two-layer spiking recurrent architecture is proposed to address temporal correlations between spike trains. The robustness of this system is supported by several open public datasets, which demonstrate state of the arts recognition accuracy and a significant reduction in network size, computational costs, and training speed. In addition to directly contributing to neuromorphic SER, this thesis proposes a novel speech-coding algorithm based on the working mechanism of humans auditory organ system. The algorithm mimics the functionality of the cochlea and successfully provides an alternative method of event-data acquisition for audio-based data. The algorithm is then further simplified and extended into an application of speech enhancement which is jointly used in the proposed SER system. This speech-enhancement method uses the lateral inhibition mechanism as a frequency coincidence detector to remove uncorrelated noise in the time-frequency spectrum. The method is shown to be effective by experiments for up to six types of noise.The spiking neural networks (SNNs), as the 3rd generation of Artificial Neural Networks (ANNs), are a class of event-driven neuromorphic algorithms that potentially have a wide range of application domains and are applicable to a variety of extremely low power neuromorphic hardware. The work presented in this thesis addresses the challenges of human gesture recognition using novel SNN algorithms. It discusses the design of these algorithms for both visual and auditory domain human gesture recognition as well as event-based pre-processing toolkits for audio signals. From the visual gesture recognition aspect, a novel SNN-based event-driven hand gesture recognition system is proposed. This system is shown to be effective in an experiment on hand gesture recognition with its spiking recurrent convolutional neural network (SCRNN) design, which combines both designed convolution operation and recurrent connectivity to maintain spatial and temporal relations with address-event-representation (AER) data. The proposed SCRNN architecture can achieve arbitrary temporal resolution, which means it can exploit temporal correlations between event collections. This design utilises a backpropagation-based training algorithm and does not suffer from gradient vanishing/explosion problems. From the audio perspective, a novel end-to-end spiking speech emotion recognition system (SER) is proposed. This system employs the MFCC as its main speech feature extractor as well as a self-designed latency coding algorithm to effciently convert the raw signal to AER input that can be used for SNN. A two-layer spiking recurrent architecture is proposed to address temporal correlations between spike trains. The robustness of this system is supported by several open public datasets, which demonstrate state of the arts recognition accuracy and a significant reduction in network size, computational costs, and training speed. In addition to directly contributing to neuromorphic SER, this thesis proposes a novel speech-coding algorithm based on the working mechanism of humans auditory organ system. The algorithm mimics the functionality of the cochlea and successfully provides an alternative method of event-data acquisition for audio-based data. The algorithm is then further simplified and extended into an application of speech enhancement which is jointly used in the proposed SER system. This speech-enhancement method uses the lateral inhibition mechanism as a frequency coincidence detector to remove uncorrelated noise in the time-frequency spectrum. The method is shown to be effective by experiments for up to six types of noise

시계열 데이터 패턴 분석을 위한 종단 심층 학습망 설계 방법론

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2019. 2. 장병탁.Pattern recognition within time series data became an important avenue of research in artificial intelligence following the paradigm shift of the fourth industrial revolution. A number of studies related to this have been conducted over the past few years, and research using deep learning techniques are becoming increasingly popular. Due to the nonstationary, nonlinear and noisy nature of time series data, it is essential to design an appropriate model to extract its significant features for pattern recognition. This dissertation not only discusses the study of pattern recognition using various hand-crafted feature engineering techniques using physiological time series signals, but also suggests an end-to-end deep learning design methodology without any feature engineering. Time series signal can be classified into signals having periodic and non-periodic characteristics in the time domain. This thesis proposes two end-to-end deep learning design methodologies for pattern recognition of periodic and non-periodic signals. The first proposed deep learning design methodology is Deep ECGNet. Deep ECGNet offers a design scheme for an end-to-end deep learning model using periodic characteristics of Electrocardiogram (ECG) signals. ECG, recorded from the electrophysiologic patterns of heart muscle during heartbeat, could be a promising candidate to provide a biomarker to estimate event-based stress level. Conventionally, the beat-to-beat alternations, heart rate variability (HRV), from ECG have been utilized to monitor the mental stress status as well as the mortality of cardiac patients. These HRV parameters have the disadvantage of having a 5-minute measurement period. In this thesis, human's stress states were estimated without special hand-crafted feature engineering using only 10-second interval data with the deep learning model. The design methodology of this model incorporates the periodic characteristics of the ECG signal into the model. The main parameters of 1D CNNs and RNNs reflecting the periodic characteristics of ECG were updated corresponding to the stress states. The experimental results proved that the proposed method yielded better performance than those of the existing HRV parameter extraction methods and spectrogram methods. The second proposed methodology is an automatic end-to-end deep learning design methodology using Bayesian optimization for non-periodic signals. Electroencephalogram (EEG) is elicited from the central nervous system (CNS) to yield genuine emotional states, even at the unconscious level. Due to the low signal-to-noise ratio (SNR) of EEG signals, spectral analysis in frequency domain has been conventionally applied to EEG studies. As a general methodology, EEG signals are filtered into several frequency bands using Fourier or wavelet analyses and these band features are then fed into a classifier. This thesis proposes an end-to-end deep learning automatic design method using optimization techniques without this basic feature engineering. Bayesian optimization is a popular optimization technique for machine learning to optimize model hyperparameters. It is often used in optimization problems to evaluate expensive black box functions. In this thesis, we propose a method to perform whole model hyperparameters and structural optimization by using 1D CNNs and RNNs as basic deep learning models and Bayesian optimization. In this way, this thesis proposes the Deep EEGNet model as a method to discriminate human emotional states from EEG signals. Experimental results proved that the proposed method showed better performance than that of conventional method based on the conventional band power feature method. In conclusion, this thesis has proposed several methodologies for time series pattern recognition problems from the feature engineering-based conventional methods to the end-to-end deep learning design methodologies with only raw time series signals. Experimental results showed that the proposed methodologies can be effectively applied to pattern recognition problems using time series data.시계열 데이터의 패턴 인식 문제는 4차 산업 혁명의 패러다임 전환과 함께 매우 중요한 인공 지능의 한 분야가 되었다. 이에 따라, 지난 몇 년간 이와 관련된 많은 연구들이 이루어져 왔으며, 최근에는 심층 학습망 (deep learning networks) 모델을 이용한 연구들이 주를 이루어 왔다. 시계열 데이터는 비정상, 비선형 그리고 잡음 (nonstationary, nonlinear and noisy) 특성으로 인하여 시계열 데이터의 패턴 인식 수행을 위해선, 데이터의 주요한 특징점을 추출하기 위한 최적화된 모델의 설계가 필수적이다. 본 논문은 대표적인 시계열 데이터인 생체 신호를 사용하여 여러 특징 벡터 추출 방법 (hand-crafted feature engineering methods)을 이용한 패턴 인식 기법에 대하여 논할 뿐만 아니라, 궁극적으로는 특징 벡터 추출 과정이 없는 종단 심층 학습망 설계 방법론에 대한 연구 내용을 담고 있다. 시계열 신호는 시간 축 상에서 크게 주기적 신호와 비주기적 신호로 구분할 수 있는데, 본 연구는 이러한 두 유형의 신호들에 대한 패턴 인식을 위해 두 가지 종단 심층 학습망에 대한 설계 방법론을 제안한다. 첫 번째 제안된 방법론을 이용해 설계된 모델은 신호의 주기적 특성을 이용한 Deep ECGNet이다. 심장 근육의 전기 생리학적 패턴으로부터 기록된 심전도 (Electrocardiogram, ECG)는 이벤트 기반 스트레스 수준을 추정하기 위한 척도 (bio marker)를 제공하는 유효한 데이터가 될 수 있다. 전통적으로 심전도의 심박수 변동성 (Herat Rate Variability, HRV) 매개변수 (parameter)는 심장 질환 환자의 정신적 스트레스 상태 및 사망률을 모니터링하는 데 사용되었다. 하지만, 표준 심박수 변동성 매개 변수는 측정 주기가 5분 이상으로, 측정 시간이 길다는 단점이 있다. 본 논문에서는 심층 학습망 모델을 이용하여 10초 간격의 ECG 데이터만을 이용하여, 추가적인 특징 벡터의 추출 과정 없이 인간의 스트레스 상태를 인식할 수 있음을 보인다. 제안된 설계 기법은 ECG 신호의 주기적 특성을 모델에 반영하였는데, ECG의 은닉 특징 추출기로 사용된 1D CNNs 및 RNNs 모델의 주요 매개 변수에 주기적 특성을 반영함으로써, 한 주기 신호의 스트레스 상태에 따른 주요 특징점을 종단 학습망 내부적으로 추출할 수 있음을 보였다. 실험 결과 제안된 방법이 기존 심박수 변동성 매개변수와 spectrogram 추출 기법 기반의 패턴 인식 방법보다 좋은 성능을 나타내고 있음을 확인할 수 있었다. 두 번째 제안된 방법론은 비 주기적이며 비정상, 비선형 그리고 잡음 특성을 지닌 신호의 패턴인식을 위한 최적 종단 심층 학습망 자동 설계 방법론이다. 뇌파 신호 (Electroencephalogram, EEG)는 중추 신경계 (CNS)에서 발생되어 무의식 상태에서도 본연의 감정 상태를 나타내는데, EEG 신호의 낮은 신호 대 잡음비 (SNR)로 인해 뇌파를 이용한 감정 상태 판정을 위해서 주로 주파수 영역의 스펙트럼 분석이 뇌파 연구에 적용되어 왔다. 통상적으로 뇌파 신호는 푸리에 (Fourier) 또는 웨이블렛 (wavelet) 분석을 사용하여 여러 주파수 대역으로 필터링 된다. 이렇게 추출된 주파수 특징 벡터는 보통 얕은 학습 분류기 (shallow machine learning classifier)의 입력으로 사용되어 패턴 인식을 수행하게 된다. 본 논문에서는 이러한 기본적인 특징 벡터 추출 과정이 없는 베이지안 최적화 (Bayesian optimization) 기법을 이용한 종단 심층 학습망 자동 설계 기법을 제안한다. 베이지안 최적화 기법은 초 매개변수 (hyperparamters)를 최적화하기 위한 기계 학습 분야의 대표적인 최적화 기법인데, 최적화 과정에서 평가 시간이 많이 소요되는 목적 함수 (expensive black box function)를 갖고 있는 최적화 문제에 적합하다. 이러한 베이지안 최적화를 이용하여 기본적인 학습 모델인 1D CNNs 및 RNNs의 전체 모델의 초 매개변수 및 구조적 최적화를 수행하는 방법을 제안하였으며, 제안된 방법론을 바탕으로 Deep EEGNet이라는 인간의 감정상태를 판별할 수 있는 모델을 제안하였다. 여러 실험을 통해 제안된 모델이 기존의 주파수 특징 벡터 (band power feature) 추출 기법 기반의 전통적인 감정 패턴 인식 방법보다 좋은 성능을 나타내고 있음을 확인할 수 있었다. 결론적으로 본 논문은 시계열 데이터를 이용한 패턴 인식문제를 여러 특징 벡터 추출 기법 기반의 전통적인 방법을 통해 설계하는 방법부터, 추가적인 특징 벡터 추출 과정 없이 원본 데이터만을 이용하여 종단 심층 학습망을 설계하는 방법까지 제안하였다. 또한, 다양한 실험을 통해 제안된 방법론이 시계열 신호 데이터를 이용한 패턴 인식 문제에 효과적으로 적용될 수 있음을 보였다.Chapter 1 Introduction 1 1.1 Pattern Recognition in Time Series 1 1.2 Major Problems in Conventional Approaches 7 1.3 The Proposed Approach and its Contribution 8 1.4 Thesis Organization 10 Chapter 2 Related Works 12 2.1 Pattern Recognition in Time Series using Conventional Methods 12 2.1.1 Time Domain Features 12 2.1.2 Frequency Domain Features 14 2.1.3 Signal Processing based on Multi-variate Empirical Mode Decomposition (MEMD) 15 2.1.4 Statistical Time Series Model (ARIMA) 18 2.2 Fundamental Deep Learning Algorithms 20 2.2.1 Convolutional Neural Networks (CNNs) 20 2.2.2 Recurrent Neural Networks (RNNs) 22 2.3 Hyper Parameters and Structural Optimization Techniques 24 2.3.1 Grid and Random Search Algorithms 24 2.3.2 Bayesian Optimization 25 2.3.3 Neural Architecture Search 28 2.4 Research Trends related to Time Series Data 29 2.4.1 Generative Model of Raw Audio Waveform 30 Chapter 3 Preliminary Researches: Patten Recognition in Time Series using Various Feature Extraction Methods 31 3.1 Conventional Methods using Time and Frequency Features: Motor Imagery Brain Response Classification 31 3.1.1 Introduction 31 3.1.2 Methods 32 3.1.3 Ensemble Classification Method (Stacking & AdaBoost) 32 3.1.4 Sensitivity Analysis 33 3.1.5 Classification Results 36 3.2 Statistical Feature Extraction Methods: ARIMA Model Based Feature Extraction Methodology 38 3.2.1 Introduction 38 3.2.2 ARIMA Model 38 3.2.3 Signal Processing 39 3.2.4 ARIMA Model Conformance Test 40 3.2.5 Experimental Results 40 3.2.6 Summary 43 3.3 Application on Specific Time Series Data: Human Stress States Recognition using Ultra-Short-Term ECG Spectral Feature 44 3.3.1 Introduction 44 3.3.2 Experiments 45 3.3.3 Classification Methods 49 3.3.4 Experimental Results 49 3.3.5 Summary 56 Chapter 4 Master Framework for Pattern Recognition in Time Series 57 4.1 The Concept of the Proposed Framework for Pattern Recognition in Time Series 57 4.1.1 Optimal Basic Deep Learning Models for the Proposed Framework 57 4.2 Two Categories for Pattern Recognition in Time Series Data 59 4.2.1 The Proposed Deep Learning Framework for Periodic Time Series Signals 59 4.2.2 The Proposed Deep Learning Framework for Non-periodic Time Series Signals 61 4.3 Expanded Models of the Proposed Master Framework for Pattern Recogntion in Time Series 63 Chapter 5 Deep Learning Model Design Methodology for Periodic Signals using Prior Knowledge: Deep ECGNet 65 5.1 Introduction 65 5.2 Materials and Methods 67 5.2.1 Subjects and Data Acquisition 67 5.2.2 Conventional ECG Analysis Methods 72 5.2.3 The Initial Setup of the Deep Learning Architecture 75 5.2.4 The Deep ECGNet 78 5.3 Experimental Results 83 5.4 Summary 98 Chapter 6 Deep Learning Model Design Methodology for Non-periodic Time Series Signals using Optimization Techniques: Deep EEGNet 100 6.1 Introduction 100 6.2 Materials and Methods 104 6.2.1 Subjects and Data Acquisition 104 6.2.2 Conventional EEG Analysis Methods 106 6.2.3 Basic Deep Learning Units and Optimization Technique 108 6.2.4 Optimization for Deep EEGNet 109 6.2.5 Deep EEGNet Architectures using the EEG Channel Grouping Scheme 111 6.3 Experimental Results 113 6.4 Summary 124 Chapter 7 Concluding Remarks 126 7.1 Summary of Thesis and Contributions 126 7.2 Limitations of the Proposed Methods 128 7.3 Suggestions for Future Works 129 Bibliography 131 초 록 139Docto