Local Geometric Distortions Resilient Watermarking Scheme Based on Symmetry

As an efficient watermark attack method, geometric distortions destroy the synchronization between watermark encoder and decoder. And the local geometric distortion is a famous challenge in the watermark field. Although a lot of geometric distortions resilient watermarking schemes have been proposed, few of them perform well against local geometric distortion like random bending attack (RBA). To address this problem, this paper proposes a novel watermark synchronization process and the corresponding watermarking scheme. In our scheme, the watermark bits are represented by random patterns. The message is encoded to get a watermark unit, and the watermark unit is flipped to generate a symmetrical watermark. Then the symmetrical watermark is embedded into the spatial domain of the host image in an additive way. In watermark extraction, we first get the theoretically mean-square error minimized estimation of the watermark. Then the auto-convolution function is applied to this estimation to detect the symmetry and get a watermark units map. According to this map, the watermark can be accurately synchronized, and then the extraction can be done. Experimental results demonstrate the excellent robustness of the proposed watermarking scheme to local geometric distortions, global geometric distortions, common image processing operations, and some kinds of combined attacks

An enhanced method based on intermediate significant bit technique for watermark images

Intermediate Significant Bit digital watermarking technique (ISB) is a new approved technique of embedding a watermark by replacing the original image pixels with new pixels. This is done by ensuring a close connection between the new pixels and the original, and at the same time, the watermark data can be protected against possible damage. One of the most popular methods used in watermarking is the Least Significant Bit (LSB). It uses a spatial domain that includes the insertion of the watermark in the LSB of the image. The problem with this method is it is not resilient to common damage, and there is the possibility of image distortion after embedding a watermark. LSB may be used through replacing one bit, two bits, or three bits; this is done by changing the specific bits without any change in the other bits in the pixel. The objective of this thesis is to formulate new algorithms for digital image watermarking with enhanced image quality and robustness by embedding two bits of watermark data into each pixel of the original image based on ISB technique. However, to understand the opposite relationship between the image quality and robustness, a tradeoff between them has been done to create a balance and to acquire the best position for the two embedding bits. Dual Intermediate Significant Bits (DISB) technique has been proposed to solve the existing LSB problem. Trial results obtained from this technique are better compared with the LSB based on the Peak Signal to Noise Ratio (PSNR) and Normalized Cross Correlation (NCC). The work in this study also contributes new mathematical equations that can study the change on the other six bits in the pixel after embedding two bits

Secure and Privacy-preserving Data Sharing in the Cloud based on Lossless Image Coding

Abstract Image and video processing in the encrypted domain has recently emerged as a promising research area to tackle privacy-related data processing issues. In particular, reversible data hiding in the encrypted domain has been suggested as a solution to store and manage digital images securely in the cloud while preserving their confidentiality. However, although efficiency has been claimed with reversible data hiding techniques in encrypted images (RDHEI), reported results show that the cloud service provider cannot add more than 1 bit per pixel (bpp) of additional data to manage stored images. This paper highlights the weakness of RDHEI as a suggested approach for secure and privacy-preserving cloud computing. In particular, we propose a new, simple, and efficient approach that offers the same level of data security and confidentiality in the cloud without the process of reversible data hiding. The proposed idea is to compress the image via a lossless image coder in order to create space before encryption. This space is then filled with a randomly generated sequence and combined with an encrypted version of the compressed bit stream to form a full resolution encrypted image in the pixel domain. The cloud service provider uses the created room in the encrypted image to add additional data and produces an encrypted image containing additional data in a similar fashion. Assessed with the lossless Embedded Block Coding with Optimized Truncation (EBCOT) algorithm on natural images, the proposed scheme has been shown to exceed the capacity of 3 bpp of additional data while maintaining data security and confidentiality

Розробка методики підвищення стійкості методів вбудови цифрових водяних знаків в цифрові зображення

A technique for increasing the stability of methods for applying digital watermark into digital images is presented. A technique for increasing the stability of methods for applying digital watermarks into digital images, based on pseudo-holographic coding and additional filtering of a digital watermark, has been developed. The technique described in this work using pseudo-holographic coding of digital watermarks is effective for all types of attacks that were considered, except for image rotation. The paper presents a statistical indicator for assessing the stability of methods for applying digital watermarks. The indicator makes it possible to comprehensively assess the resistance of the method to a certain number of attacks. An experimental study was carried out according to the proposed method. This technique is most effective when part of the image is lost. When pre-filtering a digital watermark, the most effective is the third filtering method, which is averaging over a cell with subsequent binarization. The least efficient is the first method, which is binarization and finding the statistical mode over the cell. For an affine type attack, which is an image rotation, this technique is effective only when the rotation is compensated. To estimate the rotation angle, an affine transformation matrix is found, which is obtained from a consistent set of corresponding ORB-descriptors. Using this method allows to accurately extract a digital watermark for the entire range of angles. A comprehensive assessment of the methodology for increasing the stability of the method of applying a digital watermark based on Wavelet transforms has shown that this method is 20 % better at counteracting various types of attacksПредставлена методика повышения устойчивости методов встраивания цифрового водяного в цифровые изображения. Разработана методика повышения устойчивости методов встраивания цифровых водяных знаков в цифровые изображения, основанная на псевдоголографичному кодировке и дополнительной фильтрации цифрового водяного знака. Описанная в работе методика с использованием псевдоголографичного кодирования цифровых водяных знаков является эффективной для всех типов атак, которые рассматривались, кроме поворота изображения. В работе представлен статистический показатель оценки устойчивости методов нанесения цифровых водяных знаков. Показатель позволяет комплексно оценить устойчивость метода к определенному ряду атак. Проведено экспериментальное исследование по предложенной методике. Наиболее эффективной эта методика является при потере части изображения. При предварительной фильтрации цифрового водяного знака наиболее эффективным является третий метод фильтрации, который представляет собой усреднение по ячейке с последующей бинаризацией. Наименее эффективным является первый метод, представляющий собой бинаризацию и нахождение статистической моды по ячейке. Для атаки аффинного типа, представляющей собой поворот изображения, данная методика является эффективной только при компенсации поворота. Для оценки угла поворота находится матрица аффинного преобразования, получаемого по согласованному набору соответствующих ORB-дескрипторов. Использование этого метода позволяет безошибочно выделять цифровой водяной знак для всего диапазона углов. Проведение комплексной оценки методики повышения устойчивости метода нанесения цифрового водяного знака на основе Вейвлет преобразований показало, что данная методика на 20 % лучше противодействует различным типам атакПредставлено методику підвищення стійкості методів вбудови цифрового водяного знаку в цифрові зображення. Розроблена методика підвищення стійкості методів вбудови цифрових водяних знаків в цифрові зображення, яка основана на псевдоголографічному кодуванні та додатковій фільтрації цифрового водяного знаку. Описана у роботі методика з використанням псевдоголографічного кодування цифрових водяних знаків є ефективною для всіх типів атак, що розглядалися, окрім повороту зображення. В роботі представлено статистичний показник оцінки стійкості методів нанесення цифрових водяних знаків. Показник дозволяє комплексно оцінити стійкість методу до певного ряду атак. Проведено експериментальне дослідження, щодо запропонованої методики. Найбільш ефективною ця методика є при втраті частини зображення. При попередній фільтрації цифрового водяного знаку найбільш ефективним є третій метод фільтрації, що представляє собою усереднення по клітинці з подальшою бінаризацією. Найменш ефективним є перший метод, що представляє собою бінаризацію та знаходження статистичної моди по клітинці. Для атаки афінного типу, що представляє собою поворот зображення, даний метод є ефективним тільки при компенсації повороту. Для оцінки кута повороту знаходиться матриця афінного перетворення, що отримується по узгодженому набору відповідних ORB-дескрипторів. Використання цього методу дозволяє безпомилково виділяти цифровий водяний знак для всього діапазону кутів, що досліджувалися. Проведення комплексної оцінки методики підвищення стійкості методу нанесення цифрового водяного знаку на основі Вейвлет перетворень показало, що дана методика на 20 % краще протидіє різним типам ата

Design, implementation, and evaluation of scalable content-based image retrieval techniques.

Wong, Yuk Man.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.Includes bibliographical references (leaves 95-100).Abstracts in English and Chinese.Abstract --- p.iiAcknowledgement --- p.vChapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview --- p.1Chapter 1.2 --- Contribution --- p.3Chapter 1.3 --- Organization of This Work --- p.5Chapter 2 --- Literature Review --- p.6Chapter 2.1 --- Content-based Image Retrieval --- p.6Chapter 2.1.1 --- Query Technique --- p.6Chapter 2.1.2 --- Relevance Feedback --- p.7Chapter 2.1.3 --- Previously Proposed CBIR systems --- p.7Chapter 2.2 --- Invariant Local Feature --- p.8Chapter 2.3 --- Invariant Local Feature Detector --- p.9Chapter 2.3.1 --- Harris Corner Detector --- p.9Chapter 2.3.2 --- DOG Extrema Detector --- p.10Chapter 2.3.3 --- Harris-Laplacian Corner Detector --- p.13Chapter 2.3.4 --- Harris-Affine Covariant Detector --- p.14Chapter 2.4 --- Invariant Local Feature Descriptor --- p.15Chapter 2.4.1 --- Scale Invariant Feature Transform (SIFT) --- p.15Chapter 2.4.2 --- Shape Context --- p.17Chapter 2.4.3 --- PCA-SIFT --- p.18Chapter 2.4.4 --- Gradient Location and Orientation Histogram (GLOH) --- p.19Chapter 2.4.5 --- Geodesic-Intensity Histogram (GIH) --- p.19Chapter 2.4.6 --- Experiment --- p.21Chapter 2.5 --- Feature Matching --- p.27Chapter 2.5.1 --- Matching Criteria --- p.27Chapter 2.5.2 --- Distance Measures --- p.28Chapter 2.5.3 --- Searching Techniques --- p.29Chapter 3 --- A Distributed Scheme for Large-Scale CBIR --- p.31Chapter 3.1 --- Overview --- p.31Chapter 3.2 --- Related Work --- p.33Chapter 3.3 --- Scalable Content-Based Image Retrieval Scheme --- p.34Chapter 3.3.1 --- Overview of Our Solution --- p.34Chapter 3.3.2 --- Locality-Sensitive Hashing --- p.34Chapter 3.3.3 --- Scalable Indexing Solutions --- p.35Chapter 3.3.4 --- Disk-Based Multi-Partition Indexing --- p.36Chapter 3.3.5 --- Parallel Multi-Partition Indexing --- p.37Chapter 3.4 --- Feature Representation --- p.43Chapter 3.5 --- Empirical Evaluation --- p.44Chapter 3.5.1 --- Experimental Testbed --- p.44Chapter 3.5.2 --- Performance Evaluation Metrics --- p.44Chapter 3.5.3 --- Experimental Setup --- p.45Chapter 3.5.4 --- Experiment I: Disk-Based Multi-Partition Indexing Approach --- p.45Chapter 3.5.5 --- Experiment II: Parallel-Based Multi-Partition Indexing Approach --- p.48Chapter 3.6 --- Application to WWW Image Retrieval --- p.55Chapter 3.7 --- Summary --- p.55Chapter 4 --- Image Retrieval System for IND Detection --- p.60Chapter 4.1 --- Overview --- p.60Chapter 4.1.1 --- Motivation --- p.60Chapter 4.1.2 --- Related Work --- p.61Chapter 4.1.3 --- Objective --- p.62Chapter 4.1.4 --- Contribution --- p.63Chapter 4.2 --- Database Construction --- p.63Chapter 4.2.1 --- Image Representations --- p.63Chapter 4.2.2 --- Index Construction --- p.64Chapter 4.2.3 --- Keypoint and Image Lookup Tables --- p.67Chapter 4.3 --- Database Query --- p.67Chapter 4.3.1 --- Matching Strategies --- p.68Chapter 4.3.2 --- Verification Processes --- p.71Chapter 4.3.3 --- Image Voting --- p.75Chapter 4.4 --- Performance Evaluation --- p.76Chapter 4.4.1 --- Evaluation Metrics --- p.76Chapter 4.4.2 --- Results --- p.77Chapter 4.4.3 --- Summary --- p.81Chapter 5 --- Shape-SIFT Feature Descriptor --- p.82Chapter 5.1 --- Overview --- p.82Chapter 5.2 --- Related Work --- p.83Chapter 5.3 --- SHAPE-SIFT Descriptors --- p.84Chapter 5.3.1 --- Orientation assignment --- p.84Chapter 5.3.2 --- Canonical orientation determination --- p.84Chapter 5.3.3 --- Keypoint descriptor --- p.87Chapter 5.4 --- Performance Evaluation --- p.88Chapter 5.5 --- Summary --- p.90Chapter 6 --- Conclusions and Future Work --- p.92Chapter 6.1 --- Conclusions --- p.92Chapter 6.2 --- Future Work --- p.93Chapter A --- Publication --- p.94Bibliography --- p.9

Dynamical Systems

Complex systems are pervasive in many areas of science integrated in our daily lives. Examples include financial markets, highway transportation networks, telecommunication networks, world and country economies, social networks, immunological systems, living organisms, computational systems and electrical and mechanical structures. Complex systems are often composed of a large number of interconnected and interacting entities, exhibiting much richer global scale dynamics than the properties and behavior of individual entities. Complex systems are studied in many areas of natural sciences, social sciences, engineering and mathematical sciences. This special issue therefore intends to contribute towards the dissemination of the multifaceted concepts in accepted use by the scientific community. We hope readers enjoy this pertinent selection of papers which represents relevant examples of the state of the art in present day research. [...

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