SPARSE NON-NEGATIVE DECOMPOSITION OF SPEECH POWER SPECTRA FOR FORMANT TRACKING

Many works on speech processing have dealt with auto-regressive (AR) models for spectral envelope and formant frequency estimation, mostly focusing on the estimation of the AR parameters. However, it is also interesting to be able to directly estimate the formant frequencies, or equivalently the poles of the AR filter. To tackle this issue, we propose in this paper to decompose the signal onto several bases, one for each formant, taking advantage of recent works on nonnegative matrix factorization (NMF) for the estimation stage, further refined by sparsity and smoothness penalties. The results are encouraging, and the proposed system provides formant tracks which seem robust enough to be used in different applications such as phonetic analysis, emotion detection or as visual cue for computer-aided pronunciation training applications. The model can also be extended to deal with multiple-speaker signals

Musical Audio Source Separation Based on User-Selected F0 Track

A system for user-guided audio source separation is presented in this article. Following previous works on time-frequency music representations, the proposed User Interface allows the user to select the desired audio source, by means of the assumed fundamental frequency (F0) track of that source. The system then automatically refines the selected F0 tracks, estimates and separates the corresponding source from the mixture. The interface was tested and the separation results compare positively to the results of a fully automatic system, showing that the F0 track selection improves the separation performance.LTS

Source/Filter Factorial Hidden Markov Model, with Application to Pitch and Formant Tracking

Tracking vocal tract formant frequencies ($f_p$) and estimating the fundamental frequency ($f_0$) are two tracking problems that have been tackled in many speech processing works, often independently, with applications to articulatory parameters estimations, speech analysis/synthesis or linguistics. Many works assume an auto-regressive (AR) model to fit the spectral envelope, hence indirectly estimating the formant tracks from the AR parameters. However, directly estimating the formant frequencies, or equivalently the poles of the AR filter, allows to further model the smoothness of the desired tracks. In this paper, we propose a Factorial Hidden Markov Model combined with a vocal source/filter model, with parameters naturally encoding the $f_0$ and $f_p$ tracks. Two algorithms are proposed, with two different strategies: first, a simplification of the underlying model, with a parameter estimation based on variational methods, and second, a sparse decomposition of the signal, based on Non-negative Matrix Factorization methodology. The results are comparable to state-of-the-art formant tracking algorithms. With the use of a complete production model, the proposed systems provide robust formant tracks which can be used in various applications. The algorithms could also be extended to deal with multiple-speaker signals

An overview of informed audio source separation

International audienceAudio source separation consists in recovering different unknown signals called sources by filtering their observed mixtures. In music processing, most mixtures are stereophonic songs and the sources are the individual signals played by the instruments, e.g. bass, vocals, guitar, etc. Source separation is often achieved through a classical generalized Wiener filtering, which is controlled by parameters such as the power spectrograms and the spatial locations of the sources. For an efficient filtering, those parameters need to be available and their estimation is the main challenge faced by separation algorithms. In the blind scenario, only the mixtures are available and performance strongly depends on the mixtures considered. In recent years, much research has focused on informed separation, which consists in using additional available information about the sources to improve the separation quality. In this paper, we review some recent trends in this direction

Les concessions d'exploitation forestière menacent-elles les tourbières en République démocratique du Congo ?

peer reviewed[No abstract available

Hepatitis C virus-specific cellular immune responses in individuals with no evidence of infection

The detection of hepatitis C virus (HCV)-specific T cell responses in HCV-uninfected, presumably unexposed, subjects could be due to an underestimation of the frequency of spontaneously resolving infections, as most acute HCV infections are clinically silent. To address this hypothesis, HCV-specific cellular immune responses were characterized, in individuals negative for an HCV PCR assay and humoral response, with (n = 32) or without (n = 33) risk of exposure to HCV. Uninfected volunteers (n = 20) with a chronically HCV-infected partner were included as positive controls for potential exposure to HCV and HCV infection, respectively. HCV-specific T cell responses in freshly isolated peripheral blood mononuclear cells were studied ex vivo by ELISPOT and CFSE-based proliferation assays using panels of HCV Core and NS3-derived peptides. A pool of unrelated peptides was used as a negative control, and a peptide mix of human cytomegalovirus, Epstein-Bar virus and Influenza virus as a positive control. Overall, 20% of presumably HCV-uninfected subject tested had detectable T-cell responses to the virus, a rate much higher than previous estimates of HCV prevalence in developed countries. This result would be consistent with unapparent primary HCV infections that either cleared spontaneously or remained undetected by conventional serological assays

Tremblements et mouvements anormaux aigus d'origine médicamenteuse

Les auteurs décrivent les caractéristiques sémiologiques et les étiologies des principaux mouvements anormaux aigus d'origine médicamenteuse : tremblements, myoclonies, mouvements choréïques, mouvements athétosiques, dyskinésies, dystonies, tics ainsi que les médicaments le plus fréquemment imputés. Ils discutent la conduite à tenir pratique. Les tremblements apparaissent comme les mouvements anormaux aigus d'origine médicamenteuse les plus fréquents. Devant tout mouvement anormal, on doit, a priori, rechercher une cause médicamenteuse, suspecter systématiquement et imputer en premier lieu les neuroleptiques (vrais ou "cachés") puis les autres médicaments psychoactifs. En général, les mouvements anormaux aigus d'origine médicamenteuse sont des effets indésirables régressifs à l'arrêt du médicament en cause (lorsque ceci est possible) : ils ne nécessitent pas de traitement spécifique. Les mouvements anormaux d'origine médicamenteuse sont souvent insuffisamment renseignés dans les Résumés des Caractéristiques des Produits (RCP)