D
Diego H. Milone
Researcher at National Scientific and Technical Research Council
Publications - 152
Citations - 2327
Diego H. Milone is an academic researcher from National Scientific and Technical Research Council. The author has contributed to research in topics: Computer science & Hidden Markov model. The author has an hindex of 23, co-authored 141 publications receiving 1669 citations. Previous affiliations of Diego H. Milone include National University of the Littoral & National University of Entre Ríos.
Papers
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Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis
TL;DR: A consistent decrease in performance for underrepresented genders when a minimum balance is not fulfilled raises the alarm for national agencies in charge of regulating and approving computer-assisted diagnosis systems, which should include explicit gender balance and diversity recommendations.
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Spoken emotion recognition using hierarchical classifiers
TL;DR: The spectral characteristics of emotional signals are used in order to group emotions based on acoustic rather than psychological considerations, and the proposed multiple feature hierarchical method for seven emotions improves the performance over the standard classifiers and the fixed features.
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Feature selection for face recognition based on multi-objective evolutionary wrappers
TL;DR: Experimental results show that, in comparison with other state-of-the-art approaches, the proposed approach allows to improve the classification performance, while reducing the representation dimensionality.
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Simulation of photovoltaic centrals with dynamic shading
Carlos Roberto Sánchez Reinoso,Carlos Roberto Sánchez Reinoso,Carlos Roberto Sánchez Reinoso,Diego H. Milone,R.H. Buitrago +4 more
TL;DR: In this article, the authors obtain a model for simulation of photovoltaic plants, representing the array under different conditions of dynamic shading, and investigate its effects on configurations of modules array and converters.
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Fast communication: Perceptual evaluation of blind source separation for robust speech recognition
TL;DR: The experiments were repeated using some new measures, based on the perceptual evaluation of speech quality (PESQ), which is part of the ITU P862 standard for evaluation of communication systems, and show that the PESQ-based measures outperformed all the measures reported in the previous work.