Semi-supervised {NMF} with time-frequency annotations for single-channel source separation

International audienceWe formulate a novel extension of nonnegative matrix factorization (NMF) to take into account partial information on source-specific activity in the spectrogram. This information comes in the form of masking coefficients, such as those found in an ideal binary mask. We show that state-of-the-art results in source separation may be achieved with only a limited amount of correct annotation, and furthermore our algorithm is robust to incorrect annotations. Since in practice ideal annotations are not observed, we propose several supervision scenarios to estimate the ideal mask- ing coefficients. First, manual annotations by a trained user on a dedicated graphical user interface are shown to provide satisfactory performance although they are prone to errors. Second, we investigate simple learning strate- gies to predict the Wiener coefficients based on local information around a given time-frequency bin of the spec- trogram. Results on single-channel source separation show that time-frequency annotations allow to disambiguate the source separation problem, and learned annotations open the way for a completely unsupervised learning procedure for source separation with no human intervention

Méthodes d'apprentissage de dictionnaire pour la séparation de sources audio avec un seul capteur

In this thesis we provide three main contributions to blind source separation methods based on NMF. Our first contribution is a group-sparsity inducing penalty specifically tailored for Itakura-Saito NMF. In many music tracks, there are whole intervals where only one source is active at the same time. The group-sparsity penalty we propose allows to blindly indentify these intervals and learn source specific dictionaries. As a consequence, those learned dictionaries can be used to do source separation in other parts of the track were several sources are active. These two tasks of identification and separation are performed simultaneously in one run of group-sparsity Itakura-Saito NMF. Our second contribution is an online algorithm for Itakura-Saito NMF that allows to learn dictionaries on very large audio tracks. Indeed, the memory complexity of a batch implementation NMF grows linearly with the length of the recordings and becomes prohibitive for signals longer than an hour. In contrast, our online algorithm is able to learn NMF on arbitrarily long signals with limited memory usage. Our third contribution deals user informed NMF. In short mixed signals, blind learning becomes very hard and sparsity do not retrieve interpretable dictionaries. Our contribution is very similar in spirit to inpainting. It relies on the empirical fact that, when observing the spectrogram of a mixture signal, an overwhelming proportion of it consists in regions where only one source is active. We describe an extension of NMF to take into account time-frequency localized information on the absence/presence of each source. We also investigate inferring this information with tools from machine learning.Nous proposons dans cette thèse trois contributions principales aux méthodes d'apprentissage de dictionnaire. La première est un critère de parcimonie par groupes adapté à la NMF lorsque la mesure de distorsion choisie est la divergence d'Itakura-Saito. Dans la plupart des signaux de musique on peut trouver de longs intervalles où seulement une source est active (des soli). Le critère de parcimonie par groupe que nous proposons permet de trouver automatiquement de tels segments et d'apprendre un dictionnaire adapté à chaque source. Ces dictionnaires permettent ensuite d'effectuer la tâche de séparation dans les intervalles où les sources sont mélangés. Ces deux tâches d'identification et de séparation sont effectuées simultanément en une seule passe de l'algorithme que nous proposons. Notre deuxième contribution est un algorithme en ligne pour apprendre le dictionnaire à grande échelle, sur des signaux de plusieurs heures. L'espace mémoire requis par une NMF estimée en ligne est constant alors qu'il croit linéairement avec la taille des signaux fournis dans la version standard, ce qui est impraticable pour des signaux de plus d'une heure. Notre troisième contribution touche à l'interaction avec l'utilisateur. Pour des signaux courts, l'apprentissage aveugle est particulièrement dificile, et l'apport d'information spécifique au signal traité est indispensable. Notre contribution est similaire à l'inpainting et permet de prendre en compte des annotations temps-fréquences. Elle repose sur l'observation que la quasi-totalité du spectrogramme peut etre divisé en régions spécifiquement assignées à chaque source. Nous décrivons une extension de NMF pour prendre en compte cette information et discutons la possibilité d'inférer cette information automatiquement avec des outils d'apprentissage statistique simples

A convex formulation for informed source separation in the single channel setting

Blind audio source separation is well-suited for the application of unsupervised techniques such as nonnegative matrix factorization (NMF). It has been shown that on simple examples, it retrieves sensible solutions even in the single-channel setting, which is highly ill-posed. However, it is now widely accepted that NMF alone cannot solve single-channel source separation, for real world audio signals. Several proposals have appeared recently for systems that allow the user to control the output of NMF, by specifying additional equality constraints on the coefficients of the sources in the time-frequenty domain. In this article; we show that matrix factorization problems involving these constraints can be formulated as convex problems, using the nuclear norm as a low-rank inducing penalty. We propose to solve the resulting nonsmooth convex formulation using a simple subgradient algorithm. Numerical experiments confirm that the nuclear norm penalty allows the recovery of (approximately) low-rank solutions that satisfy the additional user-imposed constraints. Moreover, for a given computational budget, we show that this algorithm matches the performance or even outperforms state-of-the-art NMF methods in terms of the quality of the estimated sources

Employment Implication of Coal Phase-Out: Revealing Transition Risks through Downscaling

International audienceThis study addresses the need to refine the assessment of employment impacts associated with coal phase-out in transition scenarios. We use a multi-sectoral macroeconomic Integrated Assessment Model enhanced by incorporating detailed data to improve productivity expectations in the coal sector. We also developed a sub-national economies dynamics module enabling the results to be scaled down, which provides qualitatively new information. The analysis quantifies job losses and their timing, highlighting regional disparities and vulnerability. The findings underscore the importance of integrating support policies for affected employees into carbon neutrality programs in order to enhance the feasibility and acceptance of transition pathways

Employment Implications of Transitioning to Net Zero: Revealing Transition Risks through Downscaling

International audienceThe rapid phase-out of fossil fuels required to meet the ambitious targets of the Paris Agreement raises concerns about sectoral decline and socioeconomic impacts. This study addresses the need to refine the assessment of employment impacts associated in transition scenarios. We investigate two climate change mitigation scenarios and a baseline using an Integrated Assessment Model (IAM) and augment the overall analysis by incorporating a data science approach. We thus developped an enhanced coal nexus within the Imaclim-R framework and a sub-national economies dynamics module. The latter enables the results to be scaled down and provide qualitatively new information. The analysis quantifies job losses and their timing, highlighting regional disparities and vulnerability. The findings underscore the importance of integrating support policies for affected employees into carbon neutrality programs. Anticipation and rapid implementation of such policies are crucial. Understanding transition risks and developing appropriate support policies can enhance the feasibility and acceptance of transition pathways

Employment Implications of Transitioning to Net Zero: Revealing Transition Risks through Downscaling

International audienceThe rapid phase-out of fossil fuels required to meet the ambitious targets of the Paris Agreement raises concerns about sectoral decline and socioeconomic impacts. This study addresses the need to refine the assessment of employment impacts associated in transition scenarios. We investigate two climate change mitigation scenarios and a baseline using an Integrated Assessment Model (IAM) and augment the overall analysis by incorporating a data science approach. We thus developped an enhanced coal nexus within the Imaclim-R framework and a sub-national economies dynamics module. The latter enables the results to be scaled down and provide qualitatively new information. The analysis quantifies job losses and their timing, highlighting regional disparities and vulnerability. The findings underscore the importance of integrating support policies for affected employees into carbon neutrality programs. Anticipation and rapid implementation of such policies are crucial. Understanding transition risks and developing appropriate support policies can enhance the feasibility and acceptance of transition pathways

Employment Implication of Coal Phase-Out: Revealing Transition Risks through Downscaling

International audienceThis study addresses the need to refine the assessment of employment impacts associated with coal phase-out in transition scenarios. We use a multi-sectoral macroeconomic Integrated Assessment Model enhanced by incorporating detailed data to improve productivity expectations in the coal sector. We also developed a sub-national economies dynamics module enabling the results to be scaled down, which provides qualitatively new information. The analysis quantifies job losses and their timing, highlighting regional disparities and vulnerability. The findings underscore the importance of integrating support policies for affected employees into carbon neutrality programs in order to enhance the feasibility and acceptance of transition pathways

Le financement des services essentiels dans les villes pauvres

Consultable sur Internet : http://www.iddri.org/Publications/Publications-scientifiques-et-autres/BenoitLEFEVRE_EcoFinanciere.pdfNational audienceL'article s'intéresse aux services publics dans les pays émergents (exemple de l'Inde et du Maroc) concernant les services de l'eau, de l'électricité et des transports