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Automatic Feature Selection in the SOPFs Dissolution Profiles Prediction Problem.

, , , and . BIOINFORMATICS, page 52-58. SciTePress, (2017)

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Automatic Selection of Acoustic and Non-Linear Dynamic Features in Voice Signals for Hypernasality Detection., , , , , , and . INTERSPEECH, page 529-532. ISCA, (2011)Automatic detection of parkinson's disease from continuous speech recorded in non-controlled noise conditions., , , , , and . INTERSPEECH, page 105-109. ISCA, (2015)Objective measurements to evaluate glottal space segmentation from laryngeal images., , , , , and . EMBC, page 5396-5399. IEEE, (2012)Towards an automatic monitoring of the neurological state of Parkinson's patients from speech., , , , , , , and . ICASSP, page 6490-6494. IEEE, (2016)Advances in Parkinson's Disease detection and assessment using voice and speech: A review of the articulatory and phonatory aspects., , , , and . Biomed. Signal Process. Control., (2021)Nonlinear Dynamics for Hypernasality Detection., , , , , and . NOLISP, volume 7015 of Lecture Notes in Computer Science, page 207-214. Springer, (2011)Entropies from Markov Models as Complexity Measures of Embedded Attractors., and . Entropy, 17 (6): 3595-3620 (2015)Automatic Detection of Pathological Voices Using Complexity Measures, Noise Parameters, and Mel-Cepstral Coefficients., , , , and . IEEE Trans. Biomed. Eng., 58 (2): 370-379 (2011)Effects of Audio Compression in Automatic Detection of Voice Pathologies., , , , , and . IEEE Trans. Biomed. Eng., 55 (12): 2831-2835 (2008)Automatic GRBAS assessment using complexity measures and a multiclass GMM-based detector., , , , and . MAVEBA, page 111-114. Firenze University Press / ISCA, (2011)