Masoumeh P. Ghaemmaghami

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In this paper, a meter classification system has been proposed for Persian poems based on features extracted from uttered poem. In the first stage, the utterance has been segmented into syllables using three features, pitch frequency and modified energy of each frame of the utterance and its temporal variations. In the second stage, each syllable is(More)
We propose an efficient and effective nonlinear feature domain noise suppression algorithm, motivated by the minimum mean square error (MMSE) optimization criterion. Multi Layer Perceptron (MLP) neural network in the log spectral domain minimizes the difference between noisy and clean speech. By using this method as a pre-processing stage of a speech(More)
In this paper, we have proposed an efficient and effective nonlinear feature domain noise suppression algorithm, motivated by the minimum mean square error (MMSE) optimization criterion. A Multi Layer Perceptron (MLP) neural network in the log spectral domain has been employed to minimize the difference between noisy and clean speech. By using this method,(More)
(DCT) is a powerful transform to extract features from a face image. It is requisite to discriminate classes using extracted DCT features. Some low frequency DCT coefficients are selected and given as input for Discrimination analysis. We used DCT for feature extraction, low frequency DCT coefficients are selected since they carry most of the information,(More)
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