Modifying the Symbolic Aggregate Approximation Method to Capture Segment Trend Information

The Symbolic Aggregate approXimation (SAX) is a very popular symbolic dimensionality reduction technique of time series data, as it has several advantages over other dimensionality reduction techniques. One of its major advantages is its efficiency, as it uses precomputed distances. The other main advantage is that in SAX the distance measure defined on the reduced space lower bounds the distance measure defined on the original space. This enables SAX to return exact results in query-by-content tasks. Yet SAX has an inherent drawback, which is its inability to capture segment trend information. Several researchers have attempted to enhance SAX by proposing modifications to include trend information. However, this comes at the expense of giving up on one or more of the advantages of SAX. In this paper we investigate three modifications of SAX to add trend capturing ability to it. These modifications retain the same features of SAX in terms of simplicity, efficiency, as well as the exact results it returns. They are simple procedures based on a different segmentation of the time series than that used in classic-SAX. We test the performance of these three modifications on 45 time series datasets of different sizes, dimensions, and nature, on a classification task and we compare it to that of classic-SAX. The results we obtained show that one of these modifications manages to outperform classic-SAX and that another one slightly gives better results than classic-SAX.Comment: International Conference on Modeling Decisions for Artificial Intelligence - MDAI 2020: Modeling Decisions for Artificial Intelligence pp 230-23

Routine Modeling with Time Series Metric Learning

version éditeur : https://rd.springer.com/chapter/10.1007/978-3-030-30484-3_47International audienceTraditionally, the automatic recognition of human activities is performed with supervised learning algorithms on limited sets of specific activities. This work proposes to recognize recurrent activity patterns, called routines, instead of precisely defined activities. The modeling of routines is defined as a metric learning problem, and an architecture, called SS2S, based on sequence-to-sequence models is proposed to learn a distance between time series. This approach only relies on inertial data and is thus non intrusive and preserves privacy. Experimental results show that a clustering algorithm provided with the learned distance is able to recover daily routines

Non-speech voice for sonic interaction: a catalogue

This paper surveys the uses of non-speech voice as an interaction modality within sonic applications. Three main contexts of use have been identified: sound retrieval, sound synthesis and control, and sound design. An overview of different choices and techniques regarding the style of interaction, the selection of vocal features and their mapping to sound features or controls is here displayed. A comprehensive collection of examples instantiates the use of non-speech voice in actual tools for sonic interaction. It is pointed out that while voice-based techniques are already being used proficiently in sound retrieval and sound synthesis, their use in sound design is still at an exploratory phase. An example of creation of a voice-driven sound design tool is here illustrated

Vocal fold vibratory patterns in tense versus lax phonation contrasts

This study explores the vocal fold contact patterns of one type of phonation contrast--the tense vs lax phonation contrasts of three Yi (Loloish) languages. These contrasts are interesting because neither phonation category is very different from modal voice, and because both phonations are largely independent of the languages' tonal contrasts. Electroglottographic (EGG) recordings were made in the field, and traditional EGG measures were derived. These showed many small but significant differences between the phonations, with tense phonation having greater contact quotients and briefer but slower changes in contact. Functional data analysis was then applied to entire EGG pulse shapes. The resulting first principal component was found to be mostly strongly related to the phonation contrasts, and correlated with almost all the traditional EGG measures. Unlike the traditional measures, however, this component also seems to capture differences in abruptness of contact. Furthermore, previously collected perceptual responses from native speakers of one of the languages correlated better with this component than with any other EGG measure or any acoustic measure. The differences between these tense and lax phonations are not large, but apparently they are consistent enough, and perceptually robust enough, to support this linguistic contrast

Excel Interface for the Kansas Geological Survey Slug Test Model

The Kansas Geological Survey (KGS) developed a semi-analytical solution for slug tests that incorporates the effects of partial penetration, anisotropy, and the presence of variable conductivity well skins. The solution can simulate either confined or unconfined conditions. The original model, written in FORTRAN, has a text-based interface with rigid input requirements and limited output options. We recreated the main routine for the KGS model as a Visual Basic macro that runs in most versions of Microsoft Excel and built a simple-to-use Excel spreadsheet interface that automatically displays the graphical results of the test. A comparison of the output from the original FORTRAN code to that of the new Excel spreadsheet version for three cases produced identical results

Texture analysis of silicon with an heterogeneous morphology used for the photovoltaic conversion by neutron diffraction

Polycrystalline silicon ingots, obtained by rapid solidification according to Bridgman method without seed crystal, are studied. The texture in the bulk, determined using the transmission neutron diffraction, is characterized from the direct pole figures plotted for the four families of the low indices lattice planes (400), (220), (111) and (113). An accurate exploring mode has been defined for this heterogeneous material. The use of smoothing computer programs on the pole densities is necessary to calculate the Orientation Distribution Function (O.D.F.) by spherical harmonics analysis.L'étude a porté sur un matériau polycristallin massif obtenu par refroidissement rapide dans un gradient thermique selon le procédé de Bridgman sans germe. La texture en volume, étudiée par diffraction neutronique en transmission, est caractérisée à partir des figures de pôles directes relatives aux quatre families de plans cristallographiques : (400 ), (220 ), (111 ) et (113 ). Un mode d'exploration précis a été défini pour ce matériau hétérogène. Le calcul de la Fonction de Distribution des Orientations Cristallines ne peut se faire qu'après utilisation d'une technique de lissage des densités de pôles

Contribution to the study of pharyngeal, epiglottal and laryngeal consonants

International audienc

Cross-modal variational inference for bijective signal-symbol translation

Extraction of symbolic information from signals is an active field of research enabling numerous applications especially in the Musical Information Retrieval domain. This complex task, that is also related to other topics such as pitch extraction or instrument recognition, is a demanding subject that gave birth to numerous approaches, mostly based on advanced signal processing-based algorithms. However, these techniques are often non-generic, allowing the extraction of definite physical properties of the signal (pitch, octave), but not allowing arbitrary vocabularies or more general annotations. On top of that, these techniques are one-sided, meaning that they can extract symbolic data from an audio signal, but cannot perform the reverse process and make symbol-to-signal generation. In this paper, we propose an bijective approach for signal/symbol translation by turning this problem into a density estimation task over signal and symbolic domains, considered both as related random variables. We estimate this joint distribution with two different variational auto-encoders, one for each domain, whose inner representations are forced to match with an additive constraint, allowing both models to learn and generate separately while allowing signal-to-symbol and symbol-to-signal inference. In this article, we test our models on pitch, octave and dynamics symbols, which comprise a fundamental step towards music transcription and label-constrained audio generation. In addition to its versatility, this system is rather light during training and generation while allowing several interesting creative uses that we outline at the end of the article