972 resultados para Affective classification
Resumo:
The aim of the current study was to investigate expressive affect in children with Williams syndrome ( WS) in comparison to typically developing children in an experimental task and in spontaneous speech. Fourteen children with WS, 14 typically developing children matched to the WS group for receptive language ( LA) and 15 typically developing children matched to the WS groups for chronological age ( CA) were recruited. Affect was investigated using an experimental Output Affect task from the Profiling Elements of Prosodic Systems-Child version ( PEPS-C) battery, and by measuring pitch range and vowel durations from a spontaneous speech task. The children were also rated for level of emotional involvement by phonetically naive listeners. The WS group performed similarly to the LA and CA groups on the Output Affect task. With regard to vowel durations, the WS group was no different from the LA group; however both the WS and the LA groups were found to use significantly longer vowels than the CA group. The WS group differed significantly from both control groups on their range of pitch range and was perceived as being significantly more emotionally involved than the two control groups.
Resumo:
Background Evidence suggests a reversal of the normal left-lateralised response to speech in schizophrenia. Aims To test the brain's response to emotional prosody in schizophrenia and bipolar disorder. Method BOLD contrast functional magnetic resonance imaging of subjects while they passively listened or attended to sentences that differed in emotional prosody Results Patients with schizophrenia exhibited normal right-lateralisation of the passive response to 'pure' emotional prosody and relative left-lateralisation of the response to unfiltered emotional prosody When attending to emotional prosody, patients with schizophrenia activated the left insula more than healthy controls. When listening passively, patients with bipolar disorder demonstrated less activation of the bilateral superior temporal gyri in response to pure emotional prosody, and greater activation of the left superior temporal gyrus in response to unfiltered emotional prosody In both passive experiments, the patient groups activated different lateral temporal lobe regions. Conclusions Patients with schizophrenia and bipolar disorder may display some left-lateralisation of the normal right-lateralised temporal lobe response to emotional prosody. Declaration of interest R.M. received a studentship from Neuraxis,, and funding from the Neuroscience and Psychiatry Unit, University of Manchester.
Resumo:
Background: Family history studies in adults reveal strong familiality for the anxiety disorders with some specificity. The aim of the current study was to establish whether there was an elevated rate of anxiety disorders in the parents of children with anxiety disorders, and whether there was intergenerational specificity in the form of disorder. Methods: The mental state of a clinic sample of 85 children with anxiety disorder and their parents was systematically assessed, together with a comparison sample of 45 children with no current disorder and their parents. Results: Compared to the rate of anxiety disorder amongst parents of comparison children, the rate of current anxiety disorder in mothers of anxious children was significantly raised, as was the lifetime rate of anxiety disorder for both mothers and fathers. The mothers of children with generalised anxiety disorder, social phobia, specific phobia and separation anxiety disorder all had raised lifetime rates of the corresponding disorder, but also raised rates of others disorders. Limitations: Only 60% of the fathers of the anxious children were assessed. Conclusions: Strong familiality of anxiety disorders was confirmed, especially between child and maternal anxiety disorder. All child anxiety disorders were associated with several forms of anxiety disorder in the mother. Some specificity in the form of anxiety disorder in the child and the mother was apparent for social phobia and separation anxiety disorder. The findings have implications for the management of child anxiety. (c) 2006 Elsevier B.V. All rights reserved.
Resumo:
In this work the G(A)(0) distribution is assumed as the universal model for amplitude Synthetic Aperture (SAR) imagery data under the Multiplicative Model. The observed data, therefore, is assumed to obey a G(A)(0) (alpha; gamma, n) law, where the parameter n is related to the speckle noise, and (alpha, gamma) are related to the ground truth, giving information about the background. Therefore, maps generated by the estimation of (alpha, gamma) in each coordinate can be used as the input for classification methods. Maximum likelihood estimators are derived and used to form estimated parameter maps. This estimation can be hampered by the presence of corner reflectors, man-made objects used to calibrate SAR images that produce large return values. In order to alleviate this contamination, robust (M) estimators are also derived for the universal model. Gaussian Maximum Likelihood classification is used to obtain maps using hard-to-deal-with simulated data, and the superiority of robust estimation is quantitatively assessed.
Resumo:
In this paper, an improved stochastic discrimination (SD) is introduced to reduce the error rate of the standard SD in the context of multi-class classification problem. The learning procedure of the improved SD consists of two stages. In the first stage, a standard SD, but with shorter learning period is carried out to identify an important space where all the misclassified samples are located. In the second stage, the standard SD is modified by (i) restricting sampling in the important space; and (ii) introducing a new discriminant function for samples in the important space. It is shown by mathematical derivation that the new discriminant function has the same mean, but smaller variance than that of standard SD for samples in the important space. It is also analyzed that the smaller the variance of the discriminant function, the lower the error rate of the classifier. Consequently, the proposed improved SD improves standard SD by its capability of achieving higher classification accuracy. Illustrative examples axe provided to demonstrate the effectiveness of the proposed improved SD.
Resumo:
Computer music usually sounds mechanical; hence, if musicality and music expression of virtual actors could be enhanced according to the user’s mood, the quality of experience would be amplified. We present a solution that is based on improvisation using cognitive models, case based reasoning (CBR) and fuzzy values acting on close-to-affect-target musical notes as retrieved from CBR per context. It modifies music pieces according to the interpretation of the user’s emotive state as computed by the emotive input acquisition componential of the CALLAS framework. The CALLAS framework incorporates the Pleasure-Arousal-Dominance (PAD) model that reflects emotive state of the user and represents the criteria for the music affectivisation process. Using combinations of positive and negative states for affective dynamics, the octants of temperament space as specified by this model are stored as base reference emotive states in the case repository, each case including a configurable mapping of affectivisation parameters. Suitable previous cases are selected and retrieved by the CBR subsystem to compute solutions for new cases, affect values from which control the music synthesis process allowing for a level of interactivity that makes way for an interesting environment to experiment and learn about expression in music.
Classification of lactose and mandelic acid THz spectra using subspace and wavelet-packet algorithms
Resumo:
This work compares classification results of lactose, mandelic acid and dl-mandelic acid, obtained on the basis of their respective THz transients. The performance of three different pre-processing algorithms applied to the time-domain signatures obtained using a THz-transient spectrometer are contrasted by evaluating the classifier performance. A range of amplitudes of zero-mean white Gaussian noise are used to artificially degrade the signal-to-noise ratio of the time-domain signatures to generate the data sets that are presented to the classifier for both learning and validation purposes. This gradual degradation of interferograms by increasing the noise level is equivalent to performing measurements assuming a reduced integration time. Three signal processing algorithms were adopted for the evaluation of the complex insertion loss function of the samples under study; a) standard evaluation by ratioing the sample with the background spectra, b) a subspace identification algorithm and c) a novel wavelet-packet identification procedure. Within class and between class dispersion metrics are adopted for the three data sets. A discrimination metric evaluates how well the three classes can be distinguished within the frequency range 0. 1 - 1.0 THz using the above algorithms.