Role of Deep Learning in Mobile Ad-hoc Networks

The portable capability of MANETs has specially delighted in an unexpected expansion. A massive need for dynamic ad-hoc basis networking continues to be created by advancements in hardware design, high-speed growth in the wireless network communications infrastructure, and increased user requirements for node mobility and regional delivery processes. There are several challenging issues in mobile ad-hoc networks, such as machine learning method cannot analyze features like node mobility, channel variation, channel interference because of the absence of deep neural layers. Due to decentralized nature of mobile ad hoc networks, its necessitate to concentrate over some extremely serious issues like stability, scalability, routing based problems such as network congestion, optimal path selection, etc. and security

잠재 임베딩을 통한 시각적 스토리로부터의 서사 텍스트 생성기 학습

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2019. 2. 장병탁.The ability to understand the story is essential to make humans unique from other primates as well as animals. The capability of story understanding is crucial for AI agents to live with people in everyday life and understand their context. However, most research on story AI focuses on automated story generation based on closed worlds designed manually, which are widely used for computation authoring. Machine learning techniques on story corpora face similar problems of natural language processing such as omitting details and commonsense knowledge. Since the remarkable success of deep learning on computer vision field, increasing our interest in research on bridging between vision and language, vision-grounded story data will potentially improve the performance of story understanding and narrative text generation. Let us assume that AI agents lie in the environment in which the sensing information is input by the camera. Those agents observe the surroundings, translate them into the story in natural language, and predict the following event or multiple ones sequentially. This dissertation study on the related problems: learning stories or generating the narrative text from image streams or videos. The first problem is to generate a narrative text from a sequence of ordered images. As a solution, we introduce a GLAC Net (Global-local Attention Cascading Network). It translates from image sequences to narrative paragraphs in text as a encoder-decoder framework with sequence-to-sequence setting. It has convolutional neural networks for extracting information from images, and recurrent neural networks for text generation. We introduce visual cue encoders with stacked bidirectional LSTMs, and all of the outputs of each layer are aggregated as contextualized image vectors to extract visual clues. The coherency of the generated text is further improved by conveying (cascading) the information of the previous sentence to the next sentence serially in the decoders. We evaluate the performance of it on the Visual storytelling (VIST) dataset. It outperforms other state-of-the-art results and shows the best scores in total score and all of 6 aspects in the visual storytelling challenge with evaluation of human judges. The second is to predict the following events or narrative texts with the former parts of stories. It should be possible to predict at any step with an arbitrary length. We propose recurrent event retrieval models as a solution. They train a context accumulation function and two embedding functions, where make close the distance between the cumulative context at current time and the next probable events on a latent space. They update the cumulative context with a new event as a input using bilinear operations, and we can find the next event candidates with the updated cumulative context. We evaluate them for Story Cloze Test, they show competitive performance and the best in open-ended generation setting. Also, it demonstrates the working examples in an interactive setting. The third deals with the study on composite representation learning for semantics and order for video stories. We embed each episode as a trajectory-like sequence of events on the latent space, and propose a ViStoryNet to regenerate video stories with them (tasks of story completion). We convert event sentences to thought vectors, and train functions to make successive event embed close each other to form episodes as trajectories. Bi-directional LSTMs are trained as sequence models, and decoders to generate event sentences with GRUs. We test them experimentally with PororoQA dataset, and observe that most of episodes show the form of trajectories. We use them to complete the blocked part of stories, and they show not perfect but overall similar result. Those results above can be applied to AI agents in the living area sensing with their cameras, explain the situation as stories, infer some unobserved parts, and predict the future story.스토리를 이해하는 능력은 동물들 뿐만 아니라 다른 유인원과 인류를 구별짓는 중요한 능력이다. 인공지능이 일상생활 속에서 사람들과 함께 지내면서 그들의 생활 속 맥락을 이해하기 위해서는 스토리를 이해하는 능력이 매우 중요하다. 하지만, 기존의 스토리에 관한 연구는 언어처리의 어려움으로 인해 사전에 정의된 세계 모델 하에서 좋은 품질의 저작물을 생성하려는 기술이 주로 연구되어 왔다. 기계학습 기법을 통해 스토리를 다루려는 시도들은 대체로 자연어로 표현된 데이터에 기반할 수 밖에 없어 자연어 처리에서 겪는 문제들을 동일하게 겪는다. 이를 극복하기 위해서는 시각적 정보가 함께 연동된 데이터가 도움이 될 수 있다. 최근 딥러닝의 눈부신 발전에 힘입어 시각과 언어 사이의 관계를 다루는 연구들이 늘어나고 있다. 연구의 비전으로서, 인공지능 에이전트가 주변 정보를 카메라로 입력받는 환경 속에 놓여있는 상황을 생각해 볼 수 있다. 이 안에서 인공지능 에이전트는 주변을 관찰하면서 그에 대한 스토리를 자연어 형태로 생성하고, 생성된 스토리를 바탕으로 다음에 일어날 스토리를 한 단계에서 여러 단계까지 예측할 수 있다. 본 학위 논문에서는 사진 및 비디오 속에 나타나는 스토리(visual story)를 학습하는 방법, 내러티브 텍스트로의 변환, 가려진 사건 및 다음 사건을 추론하는 연구들을 다룬다. 첫 번째로, 여러 장의 사진이 주어졌을 때 이를 바탕으로 스토리 텍스트를 생성하는 문제(비주얼 스토리텔링)를 다룬다. 이 문제 해결을 위해 글랙넷(GLAC Net)을 제안하였다. 먼저, 사진들로부터 정보를 추출하기 위한 컨볼루션 신경망, 문장을 생성하기 위해 순환신경망을 이용한다. 시퀀스-시퀀스 구조의 인코더로서, 전체적인 이야기 구조의 표현을 위해 다계층 양방향 순환신경망을 배치하되 각 사진 별 정보를 함께 이용하기 위해 전역적-국부적 주의집중 모델을 제안하였다. 또한, 여러 문장을 생성하는 동안 맥락정보와 국부정보를 잃지 않게 하기 위해 앞선 문장 정보를 전달하는 메커니즘을 제안하였다. 위 제안 방법으로 비스트(VIST) 데이터 집합을 학습하였고, 제 1 회 시각적 스토리텔링 대회(visual storytelling challenge)에서 사람 평가를 기준으로 전체 점수 및 6 항목 별로 모두 최고점을 받았다. 두 번째로, 스토리의 일부가 문장들로 주어졌을 때 이를 바탕으로 다음 문장을 예측하는 문제를 다룬다. 임의의 길이의 스토리에 대해 임의의 위치에서 예측이 가능해야 하고, 예측하려는 단계 수에 무관하게 작동해야 한다. 이를 위한 방법으로 순환 사건 인출 모델(Recurrent Event Retrieval Models)을 제안하였다. 이 방법은 은닉 공간 상에서 현재까지 누적된 맥락과 다음에 발생할 유력 사건 사이의 거리를 가깝게 하도록 맥락누적함수와 두 개의 임베딩 함수를 학습한다. 이를 통해 이미 입력되어 있던 스토리에 새로운 사건이 입력되면 쌍선형적 연산을 통해 기존의 맥락을 개선하여 다음에 발생할 유력한 사건들을 찾는다. 이 방법으로 락스토리(ROCStories) 데이터집합을 학습하였고, 스토리 클로즈 테스트(Story Cloze Test)를 통해 평가한 결과 경쟁력 있는 성능을 보였으며, 특히 임의의 길이로 추론할 수 있는 기법 중에 최고성능을 보였다. 세 번째로, 비디오 스토리에서 사건 시퀀스 중 일부가 가려졌을 때 이를 복구하는 문제를 다룬다. 특히, 각 사건의 의미 정보와 순서를 모델의 표현 학습에 반영하고자 하였다. 이를 위해 은닉 공간 상에 각 에피소드들을 궤적 형태로 임베딩하고, 이를 바탕으로 스토리를 재생성을 하여 스토리 완성을 할 수 있는 모델인 비스토리넷(ViStoryNet)을 제안하였다. 각 에피소드를 궤적 형태를 가지게 하기 위해 사건 문장을 사고벡터(thought vector)로 변환하고, 연속 이벤트 순서 임베딩을 통해 전후 사건들이 서로 가깝게 임베딩되도록 하여 하나의 에피소드가 궤적의 모양을 가지도록 학습하였다. 뽀로로QA 데이터집합을 통해 실험적으로 결과를 확인하였다. 임베딩 된 에피소드들은 궤적 형태로 잘 나타났으며, 에피소드들을 재생성 해본 결과 전체적인 측면에서 유사한 결과를 보였다. 위 결과물들은 카메라로 입력되는 주변 정보를 바탕으로 스토리를 이해하고 일부 관측되지 않은 부분을 추론하며, 향후 스토리를 예측하는 방법들에 대응된다.Abstract i Chapter 1 Introduction 1 1.1 Story of Everyday lives in Videos and Story Understanding . . . 1 1.2 Problems to be addressed . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Approach and Contribution . . . . . . . . . . . . . . . . . . . . . 6 1.4 Organization of Dissertation . . . . . . . . . . . . . . . . . . . . . 9 Chapter 2 Background and Related Work 10 2.1 Why We Study Stories . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2 Latent Embedding . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3 Order Embedding and Ordinal Embedding . . . . . . . . . . . . 14 2.4 Comparison to Story Understanding . . . . . . . . . . . . . . . . 15 2.5 Story Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.5.1 Abstract Event Representations . . . . . . . . . . . . . . . 17 2.5.2 Seq-to-seq Attentional Models . . . . . . . . . . . . . . . . 18 2.5.3 Story Generation from Images . . . . . . . . . . . . . . . 19 Chapter 3 Visual Storytelling via Global-local Attention Cascading Networks 21 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.2 Evaluation for Visual Storytelling . . . . . . . . . . . . . . . . . . 26 3.3 Global-local Attention Cascading Networks (GLAC Net) . . . . . 27 3.3.1 Encoder: Contextualized Image Vector Extractor . . . . . 28 3.3.2 Decoder: Story Generator with Attention and Cascading Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.4.1 VIST Dataset . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.4.2 Experiment Settings . . . . . . . . . . . . . . . . . . . . . 33 3.4.3 Network Training Details . . . . . . . . . . . . . . . . . . 36 3.4.4 Qualitative Analysis . . . . . . . . . . . . . . . . . . . . . 38 3.4.5 Quantitative Analysis . . . . . . . . . . . . . . . . . . . . 38 3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Chapter 4 Common Space Learning on Cumulative Contexts and the Next Events: Recurrent Event Retrieval Models 44 4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.2 Problems of Context Accumulation . . . . . . . . . . . . . . . . . 45 4.3 Recurrent Event Retrieval Models for Next Event Prediction . . 46 4.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.4.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.4.2 Story Cloze Test . . . . . . . . . . . . . . . . . . . . . . . 52 4.4.3 Open-ended Story Generation . . . . . . . . . . . . . . . . 53 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Chapter 5 ViStoryNet: Order Embedding of Successive Events and the Networks for Story Regeneration 58 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 5.2 Order Embedding with Triple Learning . . . . . . . . . . . . . . 60 5.2.1 Embedding Ordered Objects in Sequences . . . . . . . . . 62 5.3 Problems and Contextual Events . . . . . . . . . . . . . . . . . . 62 5.3.1 Problem Definition . . . . . . . . . . . . . . . . . . . . . . 62 5.3.2 Contextual Event Vectors from Kids Videos . . . . . . . . 64 5.4 Architectures for the Story Regeneration Task . . . . . . . . . . . 67 5.4.1 Two Sentence Generators as Decoders . . . . . . . . . . . 68 5.4.2 Successive Event Order Embedding (SEOE) . . . . . . . . 68 5.4.3 Sequence Models of the Event Space . . . . . . . . . . . . 72 5.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . 73 5.5.1 Experimental setup . . . . . . . . . . . . . . . . . . . . . . 73 5.5.2 Quantitative Analysis . . . . . . . . . . . . . . . . . . . . 73 5.5.3 Qualitative Analysis . . . . . . . . . . . . . . . . . . . . . 74 5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Chapter 6 Concluding Remarks 80 6.1 Summary of Methods and Contributions . . . . . . . . . . . . . . 80 6.2 Limitation and Outlook . . . . . . . . . . . . . . . . . . . . . . . 81 6.3 Suggestions for Future Research . . . . . . . . . . . . . . . . . . . 81 초록 101Docto

Text Classification: A Review, Empirical, and Experimental Evaluation

The explosive and widespread growth of data necessitates the use of text classification to extract crucial information from vast amounts of data. Consequently, there has been a surge of research in both classical and deep learning text classification methods. Despite the numerous methods proposed in the literature, there is still a pressing need for a comprehensive and up-to-date survey. Existing survey papers categorize algorithms for text classification into broad classes, which can lead to the misclassification of unrelated algorithms and incorrect assessments of their qualities and behaviors using the same metrics. To address these limitations, our paper introduces a novel methodological taxonomy that classifies algorithms hierarchically into fine-grained classes and specific techniques. The taxonomy includes methodology categories, methodology techniques, and methodology sub-techniques. Our study is the first survey to utilize this methodological taxonomy for classifying algorithms for text classification. Furthermore, our study also conducts empirical evaluation and experimental comparisons and rankings of different algorithms that employ the same specific sub-technique, different sub-techniques within the same technique, different techniques within the same category, and categorie

INVESTIGATING IMPROVEMENTS TO MESH INDEXING

The MEDLINE database currently comprises an extensive collection of over 35 million citations, with more than 1 million records being added each year [28]. The abundance of information available in the database presents a significant challenge in identifying and locating relevant research articles on a given search topic. This has prompted the development of various techniques and approaches aimed at improving the efficiency and effectiveness of information retrieval from the MEDLINE database. A search engine devoted to the research publications on MEDLINE is called PubMed. MeSH, or Medical Subject Headings, is a restricted vocabulary used by PubMed to categorize each article. Human annotators have been used for decades, which is not only time-consuming but also expensive. Due to its enormously complex hierarchically ordered structure, MeSH indexing is a difficult problem in the machine learning domain. We propose a model which addresses all these challenges. We propose an end-to-end model that takes MeSH description into account and combines it with a Knowledge Enhanced Mask attention model to index new research papers. We also calculated the journal correlation of each MeSH term in the MeSH hierarchy

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Biomedical Data Classification with Improvised Deep Learning Architectures

With the rise of very powerful hardware and evolution of deep learning architectures, healthcare data analysis and its applications have been drastically transformed. These transformations mainly aim to aid a healthcare personnel with diagnosis and prognosis of a disease or abnormality at any given point of healthcare routine workflow. For instance, many of the cancer metastases detection depends on pathological tissue procedures and pathologist reviews. The reports of severity classification vary amongst different pathologist, which then leads to different treatment options for a patient. This labor-intensive work can lead to errors or mistreatments resulting in high cost of healthcare. With the help of machine learning and deep learning modules, some of these traditional diagnosis techniques can be improved and aid a doctor in decision making with an unbiased view. Some of such modules can help reduce the cost, shortage of an expertise, and time in identifying the disease. However, there are many other datapoints that are available with medical images, such as omics data, biomarker calculations, patient demographics and history. All these datapoints can enhance disease classification or prediction of progression with the help of machine learning/deep learning modules. However, it is very difficult to find a comprehensive dataset with all different modalities and features in healthcare setting due to privacy regulations. Hence in this thesis, we explore both medical imaging data with clinical datapoints as well as genomics datasets separately for classification tasks using combinational deep learning architectures. We use deep neural networks with 3D volumetric structural magnetic resonance images of Alzheimer Disease dataset for classification of disease. A separate study is implemented to understand classification based on clinical datapoints achieved by machine learning algorithms. For bioinformatics applications, sequence classification task is a crucial step for many metagenomics applications, however, requires a lot of preprocessing that requires sequence assembly or sequence alignment before making use of raw whole genome sequencing data, hence time consuming especially in bacterial taxonomy classification. There are only a few approaches for sequence classification tasks that mainly involve some convolutions and deep neural network. A novel method is developed using an intrinsic nature of recurrent neural networks for 16s rRNA sequence classification which can be adapted to utilize read sequences directly. For this classification task, the accuracy is improved using optimization techniques with a hybrid neural network

MANIFOLD REPRESENTATIONS OF MUSICAL SIGNALS AND GENERATIVE SPACES

Tra i diversi campi di ricerca nell\u2019ambito dell\u2019informatica musicale, la sintesi e la generazione di segnali audio incarna la pluridisciplinalita\u300 di questo settore, nutrendo insieme le pratiche scientifiche e musicale dalla sua creazione. Inerente all\u2019informatica dalla sua creazione, la generazione audio ha ispirato numerosi approcci, evolvendo colle pratiche musicale e gli progressi tecnologici e scientifici. Inoltre, alcuni processi di sintesi permettono anche il processo inverso, denominato analisi, in modo che i parametri di sintesi possono anche essere parzialmente o totalmente estratti dai suoni, dando una rappresentazione alternativa ai segnali analizzati. Per di piu\u300, la recente ascesa dei algoritmi di l\u2019apprendimento automatico ha vivamente interrogato il settore della ricerca scientifica, fornendo potenti data-centered metodi che sollevavano diversi epistemologici interrogativi, nonostante i sui efficacia. Particolarmente, un tipo di metodi di apprendimento automatico, denominati modelli generativi, si concentrano sulla generazione di contenuto originale usando le caratteristiche che hanno estratti dei dati analizzati. In tal caso, questi modelli non hanno soltanto interrogato i precedenti metodi di generazione, ma anche sul modo di integrare questi algoritmi nelle pratiche artistiche. Mentre questi metodi sono progressivamente introdotti nel settore del trattamento delle immagini, la loro applicazione per la sintesi di segnali audio e ancora molto marginale. In questo lavoro, il nostro obiettivo e di proporre un nuovo metodo di audio sintesi basato su questi nuovi tipi di generativi modelli, rafforazti dalle nuove avanzati dell\u2019apprendimento automatico. Al primo posto, facciamo una revisione dei approcci esistenti nei settori dei sistemi generativi e di sintesi sonore, focalizzando sul posto di nostro lavoro rispetto a questi disciplini e che cosa possiamo aspettare di questa collazione. In seguito, studiamo in maniera piu\u300 precisa i modelli generativi, e come possiamo utilizzare questi recenti avanzati per l\u2019apprendimento di complesse distribuzione di suoni, in un modo che sia flessibile e nel flusso creativo del utente. Quindi proponiamo un processo di inferenza / generazione, il quale rifletta i processi di analisi/sintesi che sono molto usati nel settore del trattamento del segnale audio, usando modelli latenti, che sono basati sull\u2019utilizzazione di un spazio continuato di alto livello, che usiamo per controllare la generazione. Studiamo dapprima i risultati preliminari ottenuti con informazione spettrale estratte da diversi tipi di dati, che valutiamo qualitativamente e quantitativamente. Successiva- mente, studiamo come fare per rendere questi metodi piu\u300 adattati ai segnali audio, fronteggiando tre diversi aspetti. Primo, proponiamo due diversi metodi di regolarizzazione di questo generativo spazio che sono specificamente sviluppati per l\u2019audio : una strategia basata sulla traduzione segnali / simboli, e una basata su vincoli percettivi. Poi, proponiamo diversi metodi per fronteggiare il aspetto temporale dei segnali audio, basati sull\u2019estrazione di rappresentazioni multiscala e sulla predizione, che permettono ai generativi spazi ottenuti di anche modellare l\u2019aspetto dinamico di questi segnali. Per finire, cambiamo il nostro approccio scientifico per un punto di visto piu\u301 ispirato dall\u2019idea di ricerca e creazione. Primo, descriviamo l\u2019architettura e il design della nostra libreria open-source, vsacids, sviluppata per permettere a esperti o non-esperti musicisti di provare questi nuovi metodi di sintesi. Poi, proponiamo una prima utilizzazione del nostro modello con la creazione di una performance in real- time, chiamata \ue6go, basata insieme sulla nostra libreria vsacids e sull\u2019uso di une agente di esplorazione, imparando con rinforzo nel corso della composizione. Finalmente, tramo dal lavoro presentato alcuni conclusioni sui diversi modi di migliorare e rinforzare il metodo di sintesi proposto, nonche\u301 eventuale applicazione artistiche.Among the diverse research fields within computer music, synthesis and generation of audio signals epitomize the cross-disciplinarity of this domain, jointly nourishing both scientific and artistic practices since its creation. Inherent in computer music since its genesis, audio generation has inspired numerous approaches, evolving both with musical practices and scientific/technical advances. Moreover, some syn- thesis processes also naturally handle the reverse process, named analysis, such that synthesis parameters can also be partially or totally extracted from actual sounds, and providing an alternative representation of the analyzed audio signals. On top of that, the recent rise of machine learning algorithms earnestly questioned the field of scientific research, bringing powerful data-centred methods that raised several epistemological questions amongst researchers, in spite of their efficiency. Especially, a family of machine learning methods, called generative models, are focused on the generation of original content using features extracted from an existing dataset. In that case, such methods not only questioned previous approaches in generation, but also the way of integrating this methods into existing creative processes. While these new generative frameworks are progressively introduced in the domain of image generation, the application of such generative techniques in audio synthesis is still marginal. In this work, we aim to propose a new audio analysis-synthesis framework based on these modern generative models, enhanced by recent advances in machine learning. We first review existing approaches, both in sound synthesis and in generative machine learning, and focus on how our work inserts itself in both practices and what can be expected from their collation. Subsequently, we focus a little more on generative models, and how modern advances in the domain can be exploited to allow us learning complex sound distributions, while being sufficiently flexible to be integrated in the creative flow of the user. We then propose an inference / generation process, mirroring analysis/synthesis paradigms that are natural in the audio processing domain, using latent models that are based on a continuous higher-level space, that we use to control the generation. We first provide preliminary results of our method applied on spectral information, extracted from several datasets, and evaluate both qualitatively and quantitatively the obtained results. Subsequently, we study how to make these methods more suitable for learning audio data, tackling successively three different aspects. First, we propose two different latent regularization strategies specifically designed for audio, based on and signal / symbol translation and perceptual constraints. Then, we propose different methods to address the inner temporality of musical signals, based on the extraction of multi-scale representations and on prediction, that allow the obtained generative spaces that also model the dynamics of the signal. As a last chapter, we swap our scientific approach to a more research & creation-oriented point of view: first, we describe the architecture and the design of our open-source library, vsacids, aiming to be used by expert and non-expert music makers as an integrated creation tool. Then, we propose an first musical use of our system by the creation of a real-time performance, called aego, based jointly on our framework vsacids and an explorative agent using reinforcement learning to be trained during the performance. Finally, we draw some conclusions on the different manners to improve and reinforce the proposed generation method, as well as possible further creative applications.A\u300 travers les diffe\u301rents domaines de recherche de la musique computationnelle, l\u2019analysie et la ge\u301ne\u301ration de signaux audio sont l\u2019exemple parfait de la trans-disciplinarite\u301 de ce domaine, nourrissant simultane\u301ment les pratiques scientifiques et artistiques depuis leur cre\u301ation. Inte\u301gre\u301e a\u300 la musique computationnelle depuis sa cre\u301ation, la synthe\u300se sonore a inspire\u301 de nombreuses approches musicales et scientifiques, e\u301voluant de pair avec les pratiques musicales et les avance\u301es technologiques et scientifiques de son temps. De plus, certaines me\u301thodes de synthe\u300se sonore permettent aussi le processus inverse, appele\u301 analyse, de sorte que les parame\u300tres de synthe\u300se d\u2019un certain ge\u301ne\u301rateur peuvent e\u302tre en partie ou entie\u300rement obtenus a\u300 partir de sons donne\u301s, pouvant ainsi e\u302tre conside\u301re\u301s comme une repre\u301sentation alternative des signaux analyse\u301s. Paralle\u300lement, l\u2019inte\u301re\u302t croissant souleve\u301 par les algorithmes d\u2019apprentissage automatique a vivement questionne\u301 le monde scientifique, apportant de puissantes me\u301thodes d\u2019analyse de donne\u301es suscitant de nombreux questionnements e\u301piste\u301mologiques chez les chercheurs, en de\u301pit de leur effectivite\u301 pratique. En particulier, une famille de me\u301thodes d\u2019apprentissage automatique, nomme\u301e mode\u300les ge\u301ne\u301ratifs, s\u2019inte\u301ressent a\u300 la ge\u301ne\u301ration de contenus originaux a\u300 partir de caracte\u301ristiques extraites directement des donne\u301es analyse\u301es. Ces me\u301thodes n\u2019interrogent pas seulement les approches pre\u301ce\u301dentes, mais aussi sur l\u2019inte\u301gration de ces nouvelles me\u301thodes dans les processus cre\u301atifs existants. Pourtant, alors que ces nouveaux processus ge\u301ne\u301ratifs sont progressivement inte\u301gre\u301s dans le domaine la ge\u301ne\u301ration d\u2019image, l\u2019application de ces techniques en synthe\u300se audio reste marginale. Dans cette the\u300se, nous proposons une nouvelle me\u301thode d\u2019analyse-synthe\u300se base\u301s sur ces derniers mode\u300les ge\u301ne\u301ratifs, depuis renforce\u301s par les avance\u301es modernes dans le domaine de l\u2019apprentissage automatique. Dans un premier temps, nous examinerons les approches existantes dans le domaine des syste\u300mes ge\u301ne\u301ratifs, sur comment notre travail peut s\u2019inse\u301rer dans les pratiques de synthe\u300se sonore existantes, et que peut-on espe\u301rer de l\u2019hybridation de ces deux approches. Ensuite, nous nous focaliserons plus pre\u301cise\u301ment sur comment les re\u301centes avance\u301es accomplies dans ce domaine dans ce domaine peuvent e\u302tre exploite\u301es pour l\u2019apprentissage de distributions sonores complexes, tout en e\u301tant suffisamment flexibles pour e\u302tre inte\u301gre\u301es dans le processus cre\u301atif de l\u2019utilisateur. Nous proposons donc un processus d\u2019infe\u301rence / g\ue9n\ue9ration, refle\u301tant les paradigmes d\u2019analyse-synthe\u300se existant dans le domaine de ge\u301ne\u301ration audio, base\u301 sur l\u2019usage de mode\u300les latents continus que l\u2019on peut utiliser pour contro\u302ler la ge\u301ne\u301ration. Pour ce faire, nous e\u301tudierons de\u301ja\u300 les re\u301sultats pre\u301liminaires obtenus par cette me\u301thode sur l\u2019apprentissage de distributions spectrales, prises d\u2019ensembles de donne\u301es diversifie\u301s, en adoptant une approche a\u300 la fois quantitative et qualitative. Ensuite, nous proposerons d\u2019ame\u301liorer ces me\u301thodes de manie\u300re spe\u301cifique a\u300 l\u2019audio sur trois aspects distincts. D\u2019abord, nous proposons deux strate\u301gies de re\u301gularisation diffe\u301rentes pour l\u2019analyse de signaux audio : une base\u301e sur la traduction signal/ symbole, ainsi qu\u2019une autre base\u301e sur des contraintes perceptives. Nous passerons par la suite a\u300 la dimension temporelle de ces signaux audio, proposant de nouvelles me\u301thodes base\u301es sur l\u2019extraction de repre\u301sentations temporelles multi-e\u301chelle et sur une ta\u302che supple\u301mentaire de pre\u301diction, permettant la mode\u301lisation de caracte\u301ristiques dynamiques par les espaces ge\u301ne\u301ratifs obtenus. En dernier lieu, nous passerons d\u2019une approche scientifique a\u300 une approche plus oriente\u301e vers un point de vue recherche & cre\u301ation. Premie\u300rement, nous pre\u301senterons notre librairie open-source, vsacids, visant a\u300 e\u302tre employe\u301e par des cre\u301ateurs experts et non-experts comme un outil inte\u301gre\u301. Ensuite, nous proposons une premie\u300re utilisation musicale de notre syste\u300me par la cre\u301ation d\u2019une performance temps re\u301el, nomme\u301e \ue6go, base\u301e a\u300 la fois sur notre librarie et sur un agent d\u2019exploration appris dynamiquement par renforcement au cours de la performance. Enfin, nous tirons les conclusions du travail accompli jusqu\u2019a\u300 maintenant, concernant les possibles ame\u301liorations et de\u301veloppements de la me\u301thode de synthe\u300se propose\u301e, ainsi que sur de possibles applications cre\u301atives

Deep learning-based music source separation

Diese Dissertation befasst sich mit dem Problem der Trennung von Musikquellen durch den Einsatz von deep learning Methoden. Die auf deep learning basierende Trennung von Musikquellen wird unter drei Gesichtspunkten untersucht. Diese Perspektiven sind: die Signalverarbeitung, die neuronale Architektur und die Signaldarstellung. Aus der ersten Perspektive, soll verstanden werden, welche deep learning Modelle, die auf DNNs basieren, für die Aufgabe der Musikquellentrennung lernen, und ob es einen analogen Signalverarbeitungsoperator gibt, der die Funktionalität dieser Modelle charakterisiert. Zu diesem Zweck wird ein neuartiger Algorithmus vorgestellt. Der Algorithmus wird als NCA bezeichnet und destilliert ein optimiertes Trennungsmodell, das aus nicht-linearen Operatoren besteht, in einen einzigen linearen Operator, der leicht zu interpretieren ist. Aus der zweiten Perspektive, soll eine neuronale Netzarchitektur vorgeschlagen werden, die das zuvor erwähnte Konzept der Filterberechnung und -optimierung beinhaltet. Zu diesem Zweck wird die als Masker and Denoiser (MaD) bezeichnete neuronale Netzarchitektur vorgestellt. Die vorgeschlagene Architektur realisiert die Filteroperation unter Verwendung skip-filtering connections Verbindungen. Zusätzlich werden einige Inferenzstrategien und Optimierungsziele vorgeschlagen und diskutiert. Die Leistungsfähigkeit von MaD bei der Musikquellentrennung wird durch eine Reihe von Experimenten bewertet, die sowohl objektive als auch subjektive Bewertungsverfahren umfassen. Abschließend, der Schwerpunkt der dritten Perspektive liegt auf dem Einsatz von DNNs zum Erlernen von solchen Signaldarstellungen, für die Trennung von Musikquellen hilfreich sind. Zu diesem Zweck wird eine neue Methode vorgeschlagen. Die vorgeschlagene Methode verwendet ein neuartiges Umparametrisierungsschema und eine Kombination von Optimierungszielen. Die Umparametrisierung basiert sich auf sinusförmigen Funktionen, die interpretierbare DNN-Darstellungen fördern. Der durchgeführten Experimente deuten an, dass die vorgeschlagene Methode beim Erlernen interpretierbarer Darstellungen effizient eingesetzt werden kann, wobei der Filterprozess noch auf separate Musikquellen angewendet werden kann. Die Ergebnisse der durchgeführten Experimente deuten an, dass die vorgeschlagene Methode beim Erlernen interpretierbarer Darstellungen effizient eingesetzt werden kann, wobei der Filterprozess noch auf separate Musikquellen angewendet werden kann. Darüber hinaus der Einsatz von optimal transport (OT) Entfernungen als Optimierungsziele sind für die Berechnung additiver und klar strukturierter Signaldarstellungen.This thesis addresses the problem of music source separation using deep learning methods. The deep learning-based separation of music sources is examined from three angles. These angles are: the signal processing, the neural architecture, and the signal representation. From the first angle, it is aimed to understand what deep learning models, using deep neural networks (DNNs), learn for the task of music source separation, and if there is an analogous signal processing operator that characterizes the functionality of these models. To do so, a novel algorithm is presented. The algorithm, referred to as the neural couplings algorithm (NCA), distills an optimized separation model consisting of non-linear operators into a single linear operator that is easy to interpret. Using the NCA, it is shown that DNNs learn data-driven filters for singing voice separation, that can be assessed using signal processing. Moreover, by enabling DNNs to learn how to predict filters for source separation, DNNs capture the structure of the target source and learn robust filters. From the second angle, it is aimed to propose a neural network architecture that incorporates the aforementioned concept of filter prediction and optimization. For this purpose, the neural network architecture referred to as the Masker-and-Denoiser (MaD) is presented. The proposed architecture realizes the filtering operation using skip-filtering connections. Additionally, a few inference strategies and optimization objectives are proposed and discussed. The performance of MaD in music source separation is assessed by conducting a series of experiments that include both objective and subjective evaluation processes. Experimental results suggest that the MaD architecture, with some of the studied strategies, is applicable to realistic music recordings, and the MaD architecture has been considered one of the state-of-the-art approaches in the Signal Separation and Evaluation Campaign (SiSEC) 2018. Finally, the focus of the third angle is to employ DNNs for learning signal representations that are helpful for separating music sources. To that end, a new method is proposed using a novel re-parameterization scheme and a combination of optimization objectives. The re-parameterization is based on sinusoidal functions that promote interpretable DNN representations. Results from the conducted experimental procedure suggest that the proposed method can be efficiently employed in learning interpretable representations, where the filtering process can still be applied to separate music sources. Furthermore, the usage of optimal transport (OT) distances as optimization objectives is useful for computing additive and distinctly structured signal representations for various types of music sources