Search

Virtual reality (VR) gathers the biggest brands and consumers in the metaverse to live playful consumer experiences on the “web 4.0”, revolutionizing offers experimentation and discovery. By physically interacting, the consumer becomes the main actor, closer to the real experience. The objective of the thesis is to understand how the experiential components influence this feeling of embodiment and what are the effects of the latter on the attitude and behavior of the consumer. The hypothetico-deductive research approach leads us to build a theoretical framework in experiential marketing and then to explore the phenomenon through exploratory qualitative studies in order to develop a conceptual model. The model integrates body position (sitting vs. standing) and challenges (absence vs. moderate vs. advanced) as determinants of embodiment as well as immersion and pleasure as explanatory factors of embodiment and behavioral intentions. The quantitative study (n=328) is analyzed by a PLS structural equation model. Six experimental units make up the experiential context relating to the virtualized tourist destination visit. Results confirm the role of predictive effects and those played by mediators and moderators between certain relationships of the conceptual model. The research presents the methodological and theoretical contributions and formulates recommendations for professionals and scholars for future research., Résumé : La réalité virtuelle (RV) réunit les plus grandes marques et les consommateurs dans le métavers pour vivre des expériences de consommations ludiques sur le « web 4.0 », révolutionnant l’expérimentation et la découverte d’offres. En interagissant physiquement, le consommateur devient l’acteur principal, au plus près de l’expérience réelle. L’objectif de la thèse est de comprendre dans quelle mesure les composantes expérientielles influencent ce sentiment d’incarnation et quels sont les effets de ce dernier sur l’attitude et le comportement du consommateur. La démarche de recherche hypothético-déductive nous amène à construire un cadre théorique en marketing expérientiel puis d’explorer le phénomène par des études qualitatives à visées exploratoires afin d’élaborer un modèle conceptuel. Le modèle intègre la position du corps (assis vs. debout) et les défis (absence vs. modérés vs. avancés) tels les déterminants de l’incarnation ainsi que l’immersion et le plaisir tels les facteurs explicatifs de l’incarnation et des intentions comportementales. L’étude quantitative (n=328) est analysée par un modèle d’équations structurelles PLS. Six unités expérimentales composent le contexte expérientiel relatif la visite d’une destination touristique virtualisée. Les résultats confirment le rôle des effets prédictifs ainsi que ceux joués par des médiateurs et modérateurs entre certaines relations du modèle conceptuel. La recherche présente les apports méthodologiques et théoriques et formule des préconisations à destination des professionnels et de chercheurs dans le cadre de futures recherches.

International audience

International audience; This article concerns the separation of the music+effects (ME) track from a movie sountrack, given the observation of several international versions of the same movie. The approach chosen is strongly inspired from existing stereo audio source separation and especially from spatial filtering algorithms such as DUET that can extract a constant panned source from a mixture very efficiently. The problem is indeed similar for we aim here at separating the ME track, which is the common background of all international versions of the movie soundtrack. The algorithm has been adapted to a number of channels greater than 2. Preprocessing techniques have also been proposed to adapt the algorithm to realistic cases. The performances of the algorithm have been evaluated on realistic and synthetic cases.

International audience; Automatic musical instrument recognition is a relatively new topic in the growing field of Music Information Retrieval. Early studies mostly focused on instrument recognition from recordings of isolated notes. More recently, some studies tackled the problem of musical phrases played in solo (i.e. without accompaniment) which better covers the timbre variability of a given instrument. However, the current trend is now to deal with true polyphonic music (i.e. involving multiple instruments), which appears to be a far more difficult problem but with more practical applications. The aim of this paper is to provide an overview of the state-of-the-art in automatic musical instrument recognition with a focus on recent and innovative approaches applied to true polyphonic music. It will be shown that the traditional "bag of frames" approaches can obtain interesting results by building efficient automatic taxonomies or by using complementary information to enhance the relevant signal. We however argue that it is important to consider new directions to overcome the limitations of these traditional approaches. One of these promising directions that will be detailed concerns mid-level representations, which are based on the decomposition of the signal into a small number of sound atoms or molecules bearing explicit musical instrument labels. INTRODUCTION There is a growing interest for new means of interaction with audio information that is nowadays mostly available in digital format and stored in large databases. There is therefore a strong need for efficient audio indexing techniques that would allow the extraction of a detailed and meaningful symbolic representation directly from a digital audio recording. For music signals, this representation will include information about the metric, the harmony, the melody, the genre or the interpretation style and will ultimately be represented under the form of an enriched music sheet. The availability of such a symbolic representation opens the path for numerous Music Information Retrieval (MIR) applications including content-based search by similarity, cover songs retrieval, automatic post-remixing,. .. .

International audience; Several studies have pointed out the need of mid-level representations of music signals for information retrieval and signal processing applications. In this paper, we investigate a new representation based on sparse decomposition of the signal into a collection of instrument-specific harmonic atoms modelling notes of various pitches played by different instruments. Each atom is composed of windowed harmonic sinusoidal partials whose amplitudes are learned on a training database. An efficient Matching Pursuit algorithm was designed to extract the best atoms and to estimate the phases of their partials. Then we explain how the resulting representation can be exploited for automatic instrument recognition. Preliminary experiments on a test database of solo excerpts show promising results.

International audience; By definition, the Matching Pursuit algorithm with constant (or "flat") Gabor atoms provides a coarse estimate of frequency modulated sinusoids in music and voice signals. Chirped Gabor atoms, closer to the nature of these signals, would fit them in a finer and sparser way. Though a method for the direct analytic estimation of chirped Gabor atoms has been proposed in the past, the present article proposes an alternative method where the chirp factor and scale parameter are estimated through a regression over an iteratively selected chain of small-scale atoms defined by a Short Time Fourier Transform. This new technique suits the Matching Pursuit framework, and is compared with a "flat atoms" version of the algorithm. The influence of various frequency interpolation techniques over the sparsity of the resulting representation is also studied.

International audience; This paper addresses the usefulness of the segmentation of musical sounds into transient/non-transient parts for the task of machine recognition of musical instruments. We put into light the discriminative power of the attack-transient segments on the basis of objective criteria, consistent with the well-known psychoacoustics findings. The sound database used is composed of real-world mono-instrument phrases. Moreover, we show that, paradoxically, it is not always optimal to consider such a segmentation of the audio signal in a machine recognition system for a given decision window. Our evaluation exploits efficient automatic segmentation techniques, a wide variety of signal processing features as well as feature selection algorithms and support vector machine classification.

International audience

National audience