Learning Timbre Analogies from Unlabelled Data by Multivariate Tree Regression

This is the Author's Original Manuscript of an article whose final and definitive form, the Version of Record, has been published in the Journal of New Music Research, November 2011, copyright Taylor & Francis. The published article is available online at http://www.tandfonline.com/10.1080/09298215.2011.596938

Listeners’ perceptions of the certainty and honesty of a speaker are associated with a common prosodic signature

The success of human cooperation crucially depends on mechanisms enabling individuals to detect unreliability in their conspecifics. Yet, how such epistemic vigilance is achieved from naturalistic sensory inputs remains unclear. Here we show that listeners’ perceptions of the certainty and honesty of other speakers from their speech are based on a common prosodic signature. Using a data-driven method, we separately decode the prosodic features driving listeners’ perceptions of a speaker’s certainty and honesty across pitch, duration and loudness. We find that these two kinds of judgments rely on a common prosodic signature that is perceived independently from individuals’ conceptual knowledge and native language. Finally, we show that listeners extract this prosodic signature automatically, and that this impacts the way they memorize spoken words. These findings shed light on a unique auditory adaptation that enables human listeners to quickly detect and react to unreliability during linguistic interactions

Publisher Correction: Listeners’ perceptions of the certainty and honesty of a speaker are associated with a common prosodic signature

Correction to: Nature Communications https://doi.org/10.1038/s41467-020-20649-4, published online 8 February 2021. The original version of the Supplementary Information associated with this Article contained errors in Supplementary Figures 1, 3, 4, 5, 7 and 8 and an error in the figure legend of Supplementary Figure 8. The HTML has been updated to include a corrected version of the Supplementary Information; the original incorrect version of the Supplementary Information file can be found as Supplementary Information associated with this Correction

Listeners’ perceptions of the certainty and honesty of a speaker are associated with a common prosodic signature

The success of human cooperation crucially depends on mechanisms enabling individuals to detect unreliability in their conspecifics. Yet, how such epistemic vigilance is achieved from naturalistic sensory inputs remains unclear. Here we show that listeners’ perceptions of the certainty and honesty of other speakers from their speech are based on a common prosodic signature. Using a data-driven method, we separately decode the prosodic features driving listeners’ perceptions of a speaker’s certainty and honesty across pitch, duration and loudness. We find that these two kinds of judgments rely on a common prosodic signature that is perceived independently from individuals’ conceptual knowledge and native language. Finally, we show that listeners extract this prosodic signature automatically, and that this impacts the way they memorize spoken words. These findings shed light on a unique auditory adaptation that enables human listeners to quickly detect and react to unreliability during linguistic interactions

Adjuvant formulation for veterinary vaccines: Montanide™ Gel safety profile

AbstractSelecting the adjuvant is one of the key for the success of the vaccine in the field. Selecting a flexible adjuvant that will fit with several vaccines dedicated to one or more animal species is a source of economical efficiency. Frequently the safety or efficacy obtained with one model is different from another: there are few adjuvants fitting with the expectation of more than one animal species. Montanide™ Gel an innovative polymeric adjuvant have been tested in several animals. Our studies demonstrated the ability to use this adjuvant in dogs, cattle and pig vaccines. Three trials were performed to validate Montanide™ Gel ability to be used in cattle, pigs and dogs. Respectively, vaccines were formulated with ovalbumin in cattle, Pasteurella Multocida anatoxin and Bordetella bronchiseptica cell walls for pig and finally with parvovirus associated to two leptospira valence for dog model. All antigenic media used in the three trials were inactivated. In all trial, safety was followed through behaviour and temperature measurement as well as histology studies.Montanide™ Gel adjuvant can be used associated with a wide range of antigenic media. Nevertheless, the uses of such adjuvant need validation in avian and fish vaccines

Improving music genre classification using automatically induced harmony rules

We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 × 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates

A Zoomable Mapping of a Musical Parameter Space Using Hilbert Curves

The final publication is available at Computer Music Journal via http://dx.doi.org/10.1162/COMJ_a_0025

Study of post-deposition contamination in low-temperature deposited polysilicon films

The presence of hydrogen in polysilicon films obtained at low temperatures by hot-wire CVD and the post-deposition oxidation by air-exposure of the films are studied in this paper. The experimental results from several characterization techniques (infrared spectroscopy, X-ray photoelectron spectroscopy, secondary ion mass spectrometry and wavelength dispersive spectroscopy) showed that hydrogen and oxygen are homogeneously distributed at grain boundaries throughout the depth of the films. Hydrogen is introduced during the growth process and its concentration is higher in samples deposited at lower temperatures. Oxygen diffuses along the grain boundaries and binds to silicon atoms, mainly in Si 2O groups

Learning Combinations of Multiple Feature Representations for Music Emotion Prediction

Music consists of several structures and patterns evolving through time which greatly influences the human decoding of higher-level cognitive aspects of music like the emotions expressed in music. For tasks, such as genre, tag and emotion recognition, these structures have often been identified and used as individual and non-temporal features and representations. In this work, we address the hypothesis whether using multiple temporal and non-temporal representations of different features is beneficial for modeling music structure with the aim to predict the emotions expressed in music. We test this hypothesis by representing temporal and non-temporal structures using generative models of multiple audio features. The representations are used in a discriminative setting via the Product Probability Kernel and the Gaussian Process model enabling Multiple Kernel Learning, finding optimized combinations of both features and temporal/ non-temporal representations. We show the increased predictive performance using the combination of different features and representations along with the great interpretive prospects of this approach