1,720,989 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Paradigmes d'Apprentissage Automatique Non-Supervisés pour les Représentations de la Similarité et de la Structure Musicale.
No full textMusical structure, defined as a simplified representation of the organization of a song, is an important musicological concept, but hard to automatically estimate. This thesis presents new methods to automatically estimate the structural segmentation of a song, focusing the study of music at the barscale. By developing a new segmentation algorithm (called ``CBM'') and by comparing several unsupervised compression schemes (from linear and multilinear algebra to neural networks), paradigms introduced in this thesis result in segmentation performance outperforming those of the unsupervised State-of-the-Art methods and almost similar with those of the global State-of-the-Art, obtained with supervised machine learning algorithms. In particular, as the methods described in this thesis are unsupervised, the estimation do not rely on annotated data, lowering the bias in the estimates related to ambiguity and subjectivity (inherent to musical structure) while limiting the loss in performance compared to the best supervised methods. In addition, some of the methods studied in this thesis (in particular Nonnegative Tucker Decomposition) allow to extract automatically interpretable parts of a song which may be used for other task than the estimation of structure, and participate in the development of interpretable machine and deep learning algorithms, which is a major field of research nowadays.La structure musicale, définie comme la représentation simplifiée de l'organisation d'un morceau de musique, est un concept musicologique important mais néanmoins complexe à estimer automatiquement. Cette thèse présente de nouvelles méthodes pour estimer automatiquement la structure musicale, se focalisant sur l'étude à l'échelle de la mesure musicale. Par le développement d'un nouvel algorithme de segmentation (appelé ``CBM'') et par l'étude et la comparaison de différentes méthodes de compression non supervisées (allant de l'algèbre linéaire et multilinéaire aux réseaux de neurones), les paradigmes introduits dans cette thèse permettent d'obtenir des résultats quantitatifs dépassant l'Etat-de-l'Art non supervisé actuel et se rapprochant de l'Etat-de-l'Art global, issu de méthodes d'apprentissage avec supervision. En particulier, les méthodes décrites dans cette thèse étant non supervisées, l'estimation ne repose pas sur des bases de données annotées, permettant ainsi de mitiger les biais liés à l'ambiguïté et à la subjectivité (inhérents à la structure musicale), tout en limitant le perte en performance par rapport aux meilleures méthodes supervisées. Enfin, certaines méthodes étudiées dans cette thèse (en particulier la décomposition nonnégative en Tucker) permettent d'extraire automatiquement des parties interprétables de la chanson qui pourraient être utilisées pour d'autres tâches que l'estimation de structure, et s'intégrer dans le développement d'algorithmes interprétables d'apprentissage automatique profond, sujet de recherche majeur aujourd'hui
Audiocarnet nmf_audio_benchmark: Podcast présentant le logiciel/l toolbox nmf_audio_benchmark, permettant de facilement comparer différents algorithmes de NMF (Factorisation en Matrices Nonnégatives) sur des tâches de MIR (Music Information Retrieval, Informatique Musicale). Lien : https://github.com/ax-le/nmf_audio_benchmark/
No full textAudiocarnet pour le logiciel/la toolbox nmf_audio_benchmark, présenté dans le cadre de la journée du GDR IASIS dédié au traitement du signal audio.Lien vers le logiciel : https://github.com/ax-le/nmf_audio_benchmark
Paradigmes d'apprentissage automatique non-supervisés pour les représentations de la similarité et de la structure musicale
No full textMusical structure, defined as a simplified representation of the organization of a song, is an important musicological concept, but hard to automatically estimate. This thesis presents new methods to automatically estimate the structural segmentation of a song, focusing the study of music at the barscale. By developing a new segmentation algorithm (called ''CBM'') and by comparing several unsupervised compression schemes (from linear and multilinear algebra to neural networks), paradigms introduced in this thesis result in segmentation performance outperforming those of the unsupervised State-of-the-Art methods and almost similar with those of the global State-of-the-Art, obtained with supervised machine learning algorithms. In particular, as the methods described in this thesis are unsupervised, the estimation do not rely on annotated data, lowering the bias in the estimates related to ambiguity and subjectivity (inherent to musical structure) while limiting the loss in performance compared to the best supervised methods. In addition, some of the methods studied in this thesis (in particular Nonnegative Tucker Decomposition) allow to extract automatically interpretable parts of a song which may be used for other task than the estimation of structure, and participate in the development of interpretable machine and deep learning algorithms, which is a major field of research nowadays.La structure musicale, définie comme la représentation simplifiée de l'organisation d'un morceau de musique, est un concept musicologique important mais néanmoins complexe à estimer automatiquement. Cette thèse présente de nouvelles méthodes pour estimer automatiquement la structure musicale, se focalisant sur l'étude à l'échelle de la mesure musicale. Par le développement d'un nouvel algorithme de segmentation (appelé ''CBM'') et par l'étude et la comparaison de différentes méthodes de compression non supervisées (allant de l'algèbre linéaire et multilinéaire aux réseaux de neurones), les paradigmes introduits dans cette thèse permettent d'obtenir des résultats quantitatifs dépassant l'Etat-de-l'Art non supervisé actuel et se rapprochant de l'Etat-de-l'Art global, issu de méthodes d'apprentissage avec supervision. En particulier, les méthodes décrites dans cette thèse étant non supervisées, l'estimation ne repose pas sur des bases de données annotées, permettant ainsi de mitiger les biais liés à l'ambiguïté et à la subjectivité (inhérents à la structure musicale), tout en limitant le perte en performance par rapport aux meilleures méthodes supervisées. Enfin, certaines méthodes étudiées dans cette thèse (en particulier la décomposition nonnégative en Tucker) permettent d'extraire automatiquement des parties interprétables de la chanson qui pourraient être utilisées pour d'autres tâches que l'estimation de structure, et s'intégrer dans le développement d'algorithmes interprétables d'apprentissage automatique profond, sujet de recherche majeur aujourd'hui
nmf_audio_benchmark: Benchmarking NMF algorithms on audio tasks: Presentation of a toolbox which allows to run and compare different NMF algorithms on audio data.
No full textNational audienceIntroducing nmf_audio_benchmark (https://github.com/ax-le/nmf_audio_benchmark/), an open-source toolbox for benchmarking NMF algorithms and variants on audio tasks.This toolbox was designed because new low-rank factorization models are still being developed (in particular under new constraints or objective functions), but testing them under real conditions with audio data is not easy. In that spirit, this toolbox was primarily created to:- Provide a standardized framework for evaluating NMF-based audio processing techniques.- Offer a collection of audio datasets and pre-processing - Include a set of baseline NMF algorithms for comparison.- Enable easy integration of new NMF models for benchmarking.This toolbox is primarily designed for people developing new low-rank factorization models. Hence, it should be easy to add new NMF algorithms
nmf_audio_benchmark: Benchmarking NMF algorithms on audio tasks: Presentation of a toolbox which allows to run and compare different NMF algorithms on audio data.
No full textNational audienceIntroducing nmf_audio_benchmark (https://github.com/ax-le/nmf_audio_benchmark/), an open-source toolbox for benchmarking NMF algorithms and variants on audio tasks.This toolbox was designed because new low-rank factorization models are still being developed (in particular under new constraints or objective functions), but testing them under real conditions with audio data is not easy. In that spirit, this toolbox was primarily created to:- Provide a standardized framework for evaluating NMF-based audio processing techniques.- Offer a collection of audio datasets and pre-processing - Include a set of baseline NMF algorithms for comparison.- Enable easy integration of new NMF models for benchmarking.This toolbox is primarily designed for people developing new low-rank factorization models. Hence, it should be easy to add new NMF algorithms
nmf_audio_benchmark
No full text# NMF Audio BenchmarkThis is a repository aimed at facilitating the benchmarking of NMF-based techniques in the context of audio processing.## OverviewThis toolbox was designed because new low-rank factorization models are still being developed (in particular under new constraints or objective functions), but testing them under real conditions with audio data is not easy. In that spirit, this toolbox was primarily created to:- Provide a standardized framework for evaluating NMF-based audio processing techniques.- Offer a collection of audio datasets and pre-processing tools.- Include a set of baseline NMF algorithms for comparison.- Enable easy integration of new NMF models for benchmarking.In particular, this toolbox is primarily designed for people developing new low-rank factorization models. Hence, it should be easy to add new NMF algorithms
CBM algorithm TISMIR 2023: code and data for reproducing experiments
No full text<p>This dataset contains the data necessary to reproduce experiments presented in the TISMIR article untitled "Barwise Music Structure Analysis with the Correlation Block-Matching Segmentation Algorithm" (under publication at the time of the upload, the link will be added after publication).</p><p>In details, this zenodo upload contains:</p><ul><li>Data, i.e. precomputed data and features (the self-similarity matrices in particular, along with beats and bars estimates) required to compute the CBM algorithm,</li><li>Code, i.e. the source files and the experiments (under the form of "Notebooks") used to compute results.</li></ul><p>This upload extends the version on git (https://gitlab.imt-atlantique.fr/a23marmo/autosimilarity_segmentation/-/tree/TISMIR).</p><p> </p>
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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