Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema

In this paper, a psychologically-inspired binary cascade classification schema is proposed for speech emotion recognition. Performance is enhanced because commonly confused pairs of emotions are distinguishable from one another. Extracted features are related to statistics of pitch, formants, and energy contours, as well as spectrum, cepstrum, perceptual and temporal features, autocorrelation, MPEG-7 descriptors, Fujisakis model parameters, voice quality, jitter, and shimmer. Selected features are fed as input to K nearest neighborhood classifier and to support vector machines. Two kernels are tested for the latter: Linear and Gaussian radial basis function. The recently proposed speaker-independent experimental protocol is tested on the Berlin emotional speech database for each gender separately. The best emotion recognition accuracy, achieved by support vector machines with linear kernel, equals 87.7%, outperforming state-of-the-art approaches. Statistical analysis is first carried out with respect to the classifiers error rates and then to evaluate the information expressed by the classifiers confusion matrices. © Springer Science+Business Media, LLC 2011

Seeking Sustainability: COSA preliminary analysis of sustainability initiatives in the coffee sector

The growing economic value and consumer popularity of sustainability standards inevitably raise questions about the extent to which their structure and dynamics actually address many environmental, economic and public welfare issues. The Committee on Sustainable Assessment (COSA) was formed, in part, to develop a scientifically credible framework capable of assessing the impacts associated with the adoption of sustainability initiatives. This paper examines the pilot phase of vetting and testing the COSA method, an innovative management tool used to gather and analyze data using economic, environmental and social metrics.sustainability initiatives, standards, organic, fair trade, Rainforest, social, environmental, economic certification

Gravitational dynamics for all tensorial spacetimes carrying predictive, interpretable and quantizable matter

Only a severely restricted class of tensor fields can provide classical spacetime geometries, namely those that can carry matter field equations that are predictive, interpretable and quantizable. These three conditions on matter translate into three corresponding algebraic conditions on the underlying tensorial geometry, namely to be hyperbolic, time-orientable and energy-distinguishing. Lorentzian metrics, on which general relativity and the standard model of particle physics are built, present just the simplest tensorial spacetime geometry satisfying these conditions. The problem of finding gravitational dynamics---for the general tensorial spacetime geometries satisfying the above minimum requirements---is reformulated in this paper as a system of linear partial differential equations, in the sense that their solutions yield the actions governing the corresponding spacetime geometry. Thus the search for modified gravitational dynamics is reduced to a clear mathematical task.Comment: 47 pages, no figures, minor update

Enhancement of the electronic contribution to the low temperature specific heat of Fe/Cr magnetic multilayer

We measured the low temperature specific heat of a sputtered $(Fe_{23\AA}/Cr_{12\AA})_{33}$ magnetic multilayer, as well as separate $1000\AA$ thick Fe and Cr films. Magnetoresistance and magnetization measurements on the multilayer demonstrated antiparallel coupling between the Fe layers. Using microcalorimeters made in our group, we measured the specific heat for $4<T<30 K$ and in magnetic fields up to $8 T$ for the multilayer. The low temperature electronic specific heat coefficient of the multilayer in the temperature range $4<T<14 K$ is $\gamma_{ML}=8.4 mJ/K^{2}g-at$. This is significantly larger than that measured for the Fe or Cr films (5.4 and $3.5 mJ/K^{2}mol$ respectively). No magnetic field dependence of $\gamma_{ML}$ was observed up to $8 T$. These results can be explained by a softening of the phonon modes observed in the same data and the presence of an Fe-Cr alloy phase at the interfaces.Comment: 20 pages, 5 figure

Pinholes May Mimic Tunneling

Interest in magnetic-tunnel junctions has prompted a re-examination of tunneling measurements through thin insulating films. In any study of metal-insulator-metal trilayers, one tries to eliminate the possibility of pinholes (small areas over which the thickness of the insulator goes to zero so that the upper and lower metals of the trilayer make direct contact). Recently, we have presented experimental evidence that ferromagnet-insulator-normal trilayers that appear from current-voltage plots to be pinhole-free may nonetheless in some cases harbor pinholes. Here, we show how pinholes may arise in a simple but realistic model of film deposition and that purely classical conduction through pinholes may mimic one aspect of tunneling, the exponential decay in current with insulating thickness.Comment: 9 pages, 3 figures, plain TeX; submitted to Journal of Applied Physic

AVEC 2011 – the first international Audio/Visual Emotion Challenge

Abstract. The Audio/Visual Emotion Challenge andWorkshop (AVEC 2011) is the first competition event aimed at comparison of multimedia processing and machine learning methods for automatic audio, visual and audiovisual emotion analysis, with all participants competing under strictly the same conditions. This paper first describes the challenge par-ticipation conditions. Next follows the data used – the SEMAINE corpus – and its partitioning into train, development, and test partitions for the challenge with labelling in four dimensions, namely activity, expectation, power, and valence. Further, audio and video baseline features are intro-duced as well as baseline results that use these features for the three sub-challenges of audio, video, and audiovisual emotion recognition

Towards Emotion Recognition: A Persistent Entropy Application

Emotion recognition and classification is a very active area of research. In this paper, we present a first approach to emotion classification using persistent entropy and support vector machines. A topology-based model is applied to obtain a single real number from each raw signal. These data are used as input of a support vector machine to classify signals into 8 different emotions (calm, happy, sad, angry, fearful, disgust and surprised)