The Experts below are selected from a list of 62526 Experts worldwide ranked by ideXlab platform
Constantine Kotropoulos - One of the best experts on this subject based on the ideXlab platform.
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fast and accurate sequential floating forward feature selection with the bayes classifier applied to speech emotion recognition
Signal Processing, 2008Co-Authors: Dimitrios Ververidis, Constantine KotropoulosAbstract:This paper addresses subset feature selection performed by the sequential floating forward selection (SFFS). The criterion employed in SFFS is the Correct Classification rate of the Bayes classifier assuming that the features obey the multivariate Gaussian distribution. A theoretical analysis that models the number of Correctly classified utterances as a hypergeometric random variable enables the derivation of an accurate estimate of the variance of the Correct Classification rate during cross-validation. By employing such variance estimate, we propose a fast SFFS variant. Experimental findings on Danish emotional speech (DES) and speech under simulated and actual stress (SUSAS) databases demonstrate that SFFS computational time is reduced by 50% and the Correct Classification rate for classifying speech into emotional states for the selected subset of features varies less than the Correct Classification rate found by the standard SFFS. Although the proposed SFFS variant is tested in the framework of speech emotion recognition, the theoretical results are valid for any classifier in the context of any wrapper algorithm.
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emotional speech Classification using gaussian mixture models and the sequential floating forward selection algorithm
International Conference on Multimedia and Expo, 2005Co-Authors: Dimitrios Ververidis, Constantine KotropoulosAbstract:Emotional speech Classification can be treated as a supervised learning task where the statistical properties of emotional speech segments are the features and the emotional styles form the labels. The Akaike criterion is used for estimating automatically the number of Gaussian densities that model the probability density function of the emotional speech features. A procedure for reducing the computational burden of crossvalidation in sequential floating forward selection algorithm is proposed that applies the t-test on the probability of Correct Classification for the Bayes classifier designed for various feature sets. For the Bayes classifier, the sequential floating forward selection algorithm is found to yield a higher probability of Correct Classification by 3% than that of the sequential forward selection algorithm either taking into account the gender information or ignoring it. The experimental results indicate that the utterances from isolated words and sentences are more colored emotional than those from paragraphs. Without taking into account the gender information, the probability of Correct Classification for the Bayes classifier admits a maximum when the probability density function of emotional speech features extracted from the aforementioned utterances is modeled as a mixture of 2 Gaussian densities
Dimitrios Ververidis - One of the best experts on this subject based on the ideXlab platform.
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fast and accurate sequential floating forward feature selection with the bayes classifier applied to speech emotion recognition
Signal Processing, 2008Co-Authors: Dimitrios Ververidis, Constantine KotropoulosAbstract:This paper addresses subset feature selection performed by the sequential floating forward selection (SFFS). The criterion employed in SFFS is the Correct Classification rate of the Bayes classifier assuming that the features obey the multivariate Gaussian distribution. A theoretical analysis that models the number of Correctly classified utterances as a hypergeometric random variable enables the derivation of an accurate estimate of the variance of the Correct Classification rate during cross-validation. By employing such variance estimate, we propose a fast SFFS variant. Experimental findings on Danish emotional speech (DES) and speech under simulated and actual stress (SUSAS) databases demonstrate that SFFS computational time is reduced by 50% and the Correct Classification rate for classifying speech into emotional states for the selected subset of features varies less than the Correct Classification rate found by the standard SFFS. Although the proposed SFFS variant is tested in the framework of speech emotion recognition, the theoretical results are valid for any classifier in the context of any wrapper algorithm.
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emotional speech Classification using gaussian mixture models and the sequential floating forward selection algorithm
International Conference on Multimedia and Expo, 2005Co-Authors: Dimitrios Ververidis, Constantine KotropoulosAbstract:Emotional speech Classification can be treated as a supervised learning task where the statistical properties of emotional speech segments are the features and the emotional styles form the labels. The Akaike criterion is used for estimating automatically the number of Gaussian densities that model the probability density function of the emotional speech features. A procedure for reducing the computational burden of crossvalidation in sequential floating forward selection algorithm is proposed that applies the t-test on the probability of Correct Classification for the Bayes classifier designed for various feature sets. For the Bayes classifier, the sequential floating forward selection algorithm is found to yield a higher probability of Correct Classification by 3% than that of the sequential forward selection algorithm either taking into account the gender information or ignoring it. The experimental results indicate that the utterances from isolated words and sentences are more colored emotional than those from paragraphs. Without taking into account the gender information, the probability of Correct Classification for the Bayes classifier admits a maximum when the probability density function of emotional speech features extracted from the aforementioned utterances is modeled as a mixture of 2 Gaussian densities
B N Chatterji - One of the best experts on this subject based on the ideXlab platform.
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rotation invariant texture Classification using even symmetric gabor filters
Pattern Recognition Letters, 2003Co-Authors: Ramchandra Manthalkar, Prabir Kumar Biswas, B N ChatterjiAbstract:Rotation invariant texture features are derived from the even symmetric Gabor filtered images of texture. The feature used is modified average absolute deviation from mean. Sixty Brodatz textures rotated in 12 different directions are classified using these features. Equal number of samples are used for training and testing phase. The percentage Correct Classification is 81.02. Segmentation of texture images with rotated textures is demonstrated using the features.
Antonio Fernandez - One of the best experts on this subject based on the ideXlab platform.
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evaluation of the effects of gabor filter parameters on texture Classification
Pattern Recognition, 2007Co-Authors: Francesco Bianconi, Antonio FernandezAbstract:Gabor filtering is a widely adopted technique for texture analysis. The design of a Gabor filter bank is a complex task. In texture Classification, in particular, Gabor filters show a strong dependence on a certain number of parameters, the values of which may significantly affect the outcome of the Classification procedures. Many different approaches to Gabor filter design, based on mathematical and physiological consideration, are documented in literature. However, the effect of each parameter, as well as the effects of their interaction, remain unclear. The overall aim of this work is to investigate the effects of Gabor filter parameters on texture Classification. An extensive experimental campaign has been conducted. The outcomes of the experimental activity show a significant dependence of the percentage of Correct Classification on the smoothing parameter of the Gabor filters. On the contrary, the correlation between the number of frequencies and orientations used to define a filter bank and the percentage of Correct Classification appeared to be poor.
Ramchandra Manthalkar - One of the best experts on this subject based on the ideXlab platform.
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rotation invariant texture Classification using even symmetric gabor filters
Pattern Recognition Letters, 2003Co-Authors: Ramchandra Manthalkar, Prabir Kumar Biswas, B N ChatterjiAbstract:Rotation invariant texture features are derived from the even symmetric Gabor filtered images of texture. The feature used is modified average absolute deviation from mean. Sixty Brodatz textures rotated in 12 different directions are classified using these features. Equal number of samples are used for training and testing phase. The percentage Correct Classification is 81.02. Segmentation of texture images with rotated textures is demonstrated using the features.