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Rozprawy doktorskie na temat "Apprentissage automatique – Musique"
Fradet, Nathan. "Apprentissage automatique pour la modélisation de musique symbolique". Electronic Thesis or Diss., Sorbonne université, 2024. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2024SORUS037.pdf.
Pełny tekst źródłaSymbolic music modeling (SMM) represents the tasks performed by Deep Learning models on the symbolic music modality, among which are music generation or music information retrieval. SMM is often handled with sequential models that process data as sequences of discrete elements called tokens. This thesis study how symbolic music can be tokenized, and what are the impacts of the different ways to do it impact models performances and efficiency. Current challenges include the lack of software to perform this step, poor model efficiency and inexpressive tokens. We address these challenges by: 1) developing a complete, flexible and easy to use software library allowing to tokenize symbolic music; 2) analyzing the impact of various tokenization strategies on model performances; 3) increasing the performance and efficiency of models by leveraging large music vocabularies with the use of byte pair encoding; 4) building the first large-scale model for symbolic music generation
Jacques, Céline. "Méthodes d'apprentissage automatique pour la transcription automatique de la batterie". Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS150.
Pełny tekst źródłaThis thesis focuses on learning methods for automatic transcription of the battery. They are based on a transcription algorithm using a non-negative decomposition method, NMD. This thesis raises two main issues: the adaptation of methods to the analyzed signal and the use of deep learning. Taking into account the information of the signal analyzed in the model can be achieved by their introduction during the decomposition steps. A first approach is to reformulate the decomposition step in a probabilistic context to facilitate the introduction of a posteriori information with methods such as SI-PLCA and statistical NMD. A second approach is to implement an adaptation strategy directly in the NMD: the application of modelable filters to the patterns to model the recording conditions or the adaptation of the learned patterns directly to the signal by applying strong constraints to preserve their physical meaning. The second approach concerns the selection of the signal segments to be analyzed. It is best to analyze segments where at least one percussive event occurs. An onset detector based on a convolutional neural network (CNN) is adapted to detect only percussive onsets. The results obtained being very interesting, the detector is trained to detect only one instrument allowing the transcription of the three main drum instruments with three CNNs. Finally, the use of a CNN multi-output is studied to transcribe the part of battery with a single network
Cont, Arshia. "Modélisation de l'anticipation musicale : du temps de la musique vers la musique du temps". Phd thesis, Université Pierre et Marie Curie - Paris VI, 2008. http://tel.archives-ouvertes.fr/tel-00417565.
Pełny tekst źródłaDans le traitement de la première question, nous introduisons un cadre mathématique nommé géométrie d'informations musicales combinant la théorie de l'information, la géométrie différentielle, et l'apprentissage statistique pour représenter les contenus pertinents de l'informations musicales. La deuxième question est abordée comme un problème d'apprentissage automatique des stratégies décisionnelles dans un environnement, en employant les méthodes d'apprentissage interactif. Nous proposons pour la troisième question, une nouvelle conception du problème de synchronisation temps réel entre une partition symbolique et un musicien. Ceci nous ramène à Antescofo, un outils préliminaire d'écriture du temps et de l'interaction dans l'informatique musicale. Malgré la variété des sujets abordés dans cette thèse, la conception anticipative est la facture commune entre toutes les propositions avec les prémices de réduire la complexité structurelle et computationnelle de modélisation, et d'aider à aborder des problèmes complexes dans l'informatique musicale.
Essid, Slim. "Classification automatique des signaux audio-fréquences : reconnaissance des instruments de musique". Phd thesis, Université Pierre et Marie Curie - Paris VI, 2005. http://pastel.archives-ouvertes.fr/pastel-00002738.
Pełny tekst źródłaRousseaux, Francis. "Une contribution de l'intelligence artificielle et de l'apprentissage symbolique automatique à l'élaboration d'un modèle d'enseignement de l'écoute musicale". Phd thesis, Université Pierre et Marie Curie - Paris VI, 1990. http://tel.archives-ouvertes.fr/tel-00417579.
Pełny tekst źródłaC'est ainsi que ce thème devient un objectif d'études et de recherches : mais dans cette optique, il est nécessaire de prendre en compte l'état de l'art en informatique musicale, et d'écouter les besoins manifestés par les musiciens, afin de prendre pied sur une réelle communauté d'intérêts entre les deux disciplines.
En toute hypothèse, la musique est un objet abstrait dont il existe plusieurs représentations, aucune n'étant complète ni générale, et chacune possédant des propriétés spécifiques. Qui plus est, ces représentations ont tendance à évoluer, naître et mourir au gré des besoins des musiciens, même si la représentation sonore reste essentielle et par définition indissociable de l'objet abstrait : mais il faut bien admettre que le son musical n'est pas seul à évoquer la musique, et que si l'homme éprouve le besoin d'inventer des représentations pour mieux s'approprier le phénomène musical, il peut être enrichissant d'examiner la transposition de ce comportement aux machines.
On peut certes isoler une de ces représentations, la traduire informatiquement et lui dédier des outils : c'est ainsi que de nombreux systèmes informatiques abordent la musique. Mais il existe une approche plus typique de l'intelligence artificielle, qui consiste à chercher à atteindre l'objet abstrait à travers l'ensemble de ses représentations et de leurs relations : pour un système informatique, faire preuve d'intelligence dans ce contexte, c'est utiliser cette diversité et cette multiplicité de représentation; c'est savoir s'appuyer sur une réalité mouvante et se déplacer dans un univers d'abstractions.
Mais les représentations ne prennent leur sens qu'avec ceux qui communiquent à travers elles, qu'avec les activités qu'elles engendrent. On peut alors imaginer un système qui constituerait un véritable lieu de rencontre, de réflexion, de création, en un mot de communication : car la musique est avant tout un médium de communication. Mais quelle est la nature de ce qu'on pourra communiquer à travers un tel système ? Par exemple, on pourra s'exercer aux pratiques musicales, expérimenter de nouveaux rapports entre les représentations, en un mot s'approprier le médium musical lui-même.
Mais alors, on a besoin d'un système qui sache témoigner de ces rencontres, plus précisément qui apprenne à en témoigner; c'est là notre définition de l'apprentissage dans le contexte : on dira qu'un système apprend s'il témoigne, et éventuellement s'adapte à un univers de communication musicale. Sans cette exigence, la valeur de la communication est perdue : en effet les parties prenantes quittent le système avec leur nouvelle richesse, quelle que soit la réussite de la médiation. Aussi, l'enjeu pour un système apprenti consiste à retourner un témoignage aux musiciens, aux pédagogues et aux informaticiens, afin qu'ils puissent en tirer profit : bien entendu, on exigera de ce témoignage qu'il produise de la connaissance utile, sans se contenter de cumuls d'événements ou de faits ordonnés historiquement.
Ainsi, à travers un enseignement ouvert, il s'agira pour des élèves d'appréhender et d'expérimenter le médium musical, d'enrichir leurs connaissances et d'obtenir des explications. Pour des enseignants, il s'agira de créer et d'organiser cette médiation, et de rendre des oracles pédagogiques au système. Mais l'intelligence artificielle et l'apprentissage symbolique automatique sont les sciences de l'explication : il faut mettre en jeu la dimension cognitive qui permettra d'expertiser l'adéquation du lieu de rencontre; il faut se placer au cœur des besoins et des préoccupations des enseignants et des élèves, en tentant de formaliser les théories cognitives de la musique. On pourra même inventer des représentations à vocations cognitive et explicative : à terme, un système construit sur un tel modèle pourrait bien être capable de faire lui-même des découvertes dans ce domaine.
Bayle, Yann. "Apprentissage automatique de caractéristiques audio : application à la génération de listes de lecture thématiques". Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0087/document.
Pełny tekst źródłaThis doctoral dissertation presents, discusses and proposes tools for the automatic information retrieval in big musical databases.The main application is the supervised classification of musical themes to generate thematic playlists.The first chapter introduces the different contexts and concepts around big musical databases and their consumption.The second chapter focuses on the description of existing music databases as part of academic experiments in audio analysis.This chapter notably introduces issues concerning the variety and unequal proportions of the themes contained in a database, which remain complex to take into account in supervised classification.The third chapter explains the importance of extracting and developing relevant audio features in order to better describe the content of music tracks in these databases.This chapter explains several psychoacoustic phenomena and uses sound signal processing techniques to compute audio features.New methods of aggregating local audio features are proposed to improve song classification.The fourth chapter describes the use of the extracted audio features in order to sort the songs by themes and thus to allow the musical recommendations and the automatic generation of homogeneous thematic playlists.This part involves the use of machine learning algorithms to perform music classification tasks.The contributions of this dissertation are summarized in the fifth chapter which also proposes research perspectives in machine learning and extraction of multi-scale audio features
Bel, Bernard. "Acquisition et représentation de connaissances en musique". Phd thesis, Aix-Marseille 3, 1990. http://tel.archives-ouvertes.fr/tel-00009692.
Pełny tekst źródłaCarsault, Tristan. "Introduction of musical knowledge and qualitative analysis in chord extraction and prediction tasks with machine learning. : application to human-machine co-improvisation". Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS247.
Pełny tekst źródłaThis thesis investigates the impact of introducing musical properties in machine learning models for the extraction and inference of musical features. Furthermore, it discusses the use of musical knowledge to perform qualitative evaluations of the results. In this work, we focus on musical chords since these mid-level features are frequently used to describe harmonic progressions in Western music. Hence, amongs the variety of tasks encountered in the field of Music Information Retrieval (MIR), the two main tasks that we address are the Automatic Chord Extraction (ACE) and the inference of symbolic chord sequences. In the case of musical chords, there exists inherent strong hierarchical and functional relationships. Indeed, even if two chords do not belong to the same class, they can share the same harmonic function within a chord progression. Hence, we developed a specifically-tailored analyzer that focuses on the functional relations between chords to distinguish strong and weak errors. We define weak errors as a misclassification that still preserves the relevance in terms of harmonic function. This reflects the fact that, in contrast to strict transcription tasks, the extraction of high-level musical features is a rather subjective task. Moreover, many creative applications would benefit from a higher level of harmonic understanding rather than an increased accuracy of label classification. For instance, one of our application case is the development of a software that interacts with a musician in real-time by inferring expected chord progressions. In order to achieve this goal, we divided the project into two main tasks : a listening module and a symbolic generation module. The listening module extracts the musical structure played by the musician, where as the generative module predicts musical sequences based on the extracted features. In the first part of this thesis, we target the development of an ACE system that could emulate the process of musical structure discovery, as performed by musicians in improvisation contexts. Most ACE systems are built on the idea of extracting features from raw audio signals and, then, using these features to construct a chord classifier. This entail two major families of approaches, as either rule-based or statistical models. In this work, we identify drawbacks in the use of statistical models for ACE tasks. Then, we propose to introduce prior musical knowledge in order to account for the inherent relationships between chords directly inside the loss function of learning methods. In the second part of this thesis, we focus on learning higher-level relationships inside sequences of extracted chords in order to develop models with the ability to generate potential continuations of chord sequences. In order to introduce musical knowledge in these models, we propose both new architectures, multi-label training methods and novel data representations
Nistal, Hurlé Javier. "Exploring generative adversarial networks for controllable musical audio synthesis". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT009.
Pełny tekst źródłaAudio synthesizers are electronic musical instruments that generate artificial sounds under some parametric control. While synthesizers have evolved since they were popularized in the 70s, two fundamental challenges are still unresolved: 1) the development of synthesis systems responding to semantically intuitive parameters; 2) the design of "universal," source-agnostic synthesis techniques. This thesis researches the use of Generative Adversarial Networks (GAN) towards building such systems. The main goal is to research and develop novel tools for music production that afford intuitive and expressive means of sound manipulation, e.g., by controlling parameters that respond to perceptual properties of the sound and other high-level features. Our first work studies the performance of GANs when trained on various common audio signal representations (e.g., waveform, time-frequency representations). These experiments compare different forms of audio data in the context of tonal sound synthesis. Results show that the Magnitude and Instantaneous Frequency of the phase and the complex-valued Short-Time Fourier Transform achieve the best results. Building on this, our following work presents DrumGAN, a controllable adversarial audio synthesizer of percussive sounds. By conditioning the model on perceptual features describing high-level timbre properties, we demonstrate that intuitive control can be gained over the generation process. This work results in the development of a VST plugin generating full-resolution audio and compatible with any Digital Audio Workstation (DAW). We show extensive musical material produced by professional artists from Sony ATV using DrumGAN. The scarcity of annotations in musical audio datasets challenges the application of supervised methods to conditional generation settings. Our third contribution employs a knowledge distillation approach to extract such annotations from a pre-trained audio tagging system. DarkGAN is an adversarial synthesizer of tonal sounds that employs the output probabilities of such a system (so-called “soft labels”) as conditional information. Results show that DarkGAN can respond moderately to many intuitive attributes, even with out-of-distribution input conditioning. Applications of GANs to audio synthesis typically learn from fixed-size two-dimensional spectrogram data analogously to the "image data" in computer vision; thus, they cannot generate sounds with variable duration. In our fourth paper, we address this limitation by exploiting a self-supervised method for learning discrete features from sequential data. Such features are used as conditional input to provide step-wise time-dependent information to the model. Global consistency is ensured by fixing the input noise z (characteristic in adversarial settings). Results show that, while models trained on a fixed-size scheme obtain better audio quality and diversity, ours can competently generate audio of any duration. One interesting direction for research is the generation of audio conditioned on preexisting musical material, e.g., the generation of some drum pattern given the recording of a bass line. Our fifth paper explores a simple pretext task tailored at learning such types of complex musical relationships. Concretely, we study whether a GAN generator, conditioned on highly compressed MP3 musical audio signals, can generate outputs resembling the original uncompressed audio. Results show that the GAN can improve the quality of the audio signals over the MP3 versions for very high compression rates (16 and 32 kbit/s). As a direct consequence of applying artificial intelligence techniques in musical contexts, we ask how AI-based technology can foster innovation in musical practice. Therefore, we conclude this thesis by providing a broad perspective on the development of AI tools for music production, informed by theoretical considerations and reports from real-world AI tool usage by professional artists
Françoise, Jules. "Motion-sound Mapping By Demonstration". Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066105/document.
Pełny tekst źródłaDesigning the relationship between motion and sound is essential to the creation of interactive systems. This thesis proposes an approach to the design of the mapping between motion and sound called Mapping-by-Demonstration. Mapping-by-Demonstration is a framework for crafting sonic interactions from demonstrations of embodied associations between motion and sound. It draws upon existing literature emphasizing the importance of bodily experience in sound perception and cognition. It uses an interactive machine learning approach to build the mapping iteratively from user demonstrations. Drawing upon related work in the fields of animation, speech processing and robotics, we propose to fully exploit the generative nature of probabilistic models, from continuous gesture recognition to continuous sound parameter generation. We studied several probabilistic models under the light of continuous interaction. We examined both instantaneous (Gaussian Mixture Model) and temporal models (Hidden Markov Model) for recognition, regression and parameter generation. We adopted an Interactive Machine Learning perspective with a focus on learning sequence models from few examples, and continuously performing recognition and mapping. The models either focus on movement, or integrate a joint representation of motion and sound. In movement models, the system learns the association between the input movement and an output modality that might be gesture labels or movement characteristics. In motion-sound models, we model motion and sound jointly, and the learned mapping directly generates sound parameters from input movements. We explored a set of applications and experiments relating to real-world problems in movement practice, sonic interaction design, and music. We proposed two approaches to movement analysis based on Hidden Markov Model and Hidden Markov Regression, respectively. We showed, through a use-case in Tai Chi performance, how the models help characterizing movement sequences across trials and performers. We presented two generic systems for movement sonification. The first system allows users to craft hand gesture control strategies for the exploration of sound textures, based on Gaussian Mixture Regression. The second system exploits the temporal modeling of Hidden Markov Regression for associating vocalizations to continuous gestures. Both systems gave birth to interactive installations that we presented to a wide public, and we started investigating their interest to support gesture learning