Classification of Voice Aging Using Parameters Extracted from the Glottal Signal. - Leonardo Alfredo Forero Mendoza; Edson Cataldo; Marley M. B. R. Vellasco; Marco Silva 002 (ICANN (3), 2010)

Evolutionary features and parameter optimization of spiking neural networks for unsupervised learning. - Marco Silva 002; Adriano S. Koshiyama; Marley Maria Bernardes Rebuzzi Vellasco; Edson Cataldo (IJCNN, 2014)

Quantum-Inspired Features and Parameter Optimization of Spiking Neural Networks for a Case Study from Atmospheric. - Marcelo C. Cardoso; Marco Silva 002; Marley M. B. R. Vellasco; Edson Cataldo (INNS Conference on Big Data, 2015)

Jitter generation in voice signals produced by a two-mass stochastic mechanical model. - Edson Cataldo; Christian Soize (Biomed. Signal Process. Control., 2016)

Analysis and Characterization of GPU Benchmarks for Kernel Concurrency Efficiency. - Pablo Carvalho; Lúcia M. A. Drummond; Cristiana Bentes; Esteban Clua; Edson Cataldo; Leandro A. J. Marzulo (CARLA, 2017)

Kernel concurrency opportunities based on GPU benchmarks characterization. - Pablo Carvalho; Rommel Anatoli Quintanilla Cruz; Lúcia M. A. Drummond; Cristiana Bentes; Esteban Clua; Edson Cataldo; Leandro A. J. Marzulo (Clust. Comput., 2020)

A stochastic model of voice generation and the corresponding solution for the inverse problem using Artificial Neural Network for case with pathology in the vocal folds. - Edson Cataldo; Christian Soize (Biomed. Signal Process. Control., 2021)

Stochastic models of glottal pulses from the Rosenberg and Liljencrants-Fant models with unified parameters. - Edson Cataldo; Diego Bahiano (Comput. Speech Lang., 2021)

Adapted Weighted Linear Prediction with Attenuated Main Excitation for formant frequency estimation in high-pitched singing. - Eduardo Barrientos; Edson Cataldo (Speech Commun., 2024)