no code implementations • 4 Jun 2023 • Michael Taynnan Barros, Michelangelo Paci, Aapo Tervonen, Elisa Passini, Jussi Koivumäki, Jari Hyttinen, Kerstin Lenk
With many advancements in in silico biology in recent years, the paramount challenge is to translate the accumulated knowledge into exciting industry partnerships and clinical applications.
1 code implementation • 24 Jan 2022 • Loris Nanni, Michelangelo Paci, Sheryl Brahnam, Alessandra Lumini
These novel methods are based on the Fourier Transform (FT), the Radon Transform (RT) and the Discrete Cosine Transform (DCT).
no code implementations • 29 Mar 2021 • Loris Nanni, Gianluca Maguolo, Sheryl Brahnam, Michelangelo Paci
Because activation functions inject different nonlinearities between layers that affect performance, varying them is one method for building robust ensembles of CNNs.
no code implementations • 15 Jul 2020 • Loris Nanni, Gianluca Maguolo, Sheryl Brahnam, Michelangelo Paci
The best performing ensembles combining data augmentation techniques with different signal representations are compared and shown to outperform the best methods reported in the literature on these datasets.
no code implementations • 16 Dec 2019 • Loris Nanni, Gianluca Maguolo, Michelangelo Paci
To the best of our knowledge this is the largest study on data augmentation for CNNs in animal audio classification audio datasets using the same set of classifiers and parameters.
1 code implementation • 11 Dec 2019 • Gianluca Maguolo, Michelangelo Paci, Loris Nanni, Ludovico Bonan
Audio data augmentation is a key step in training deep neural networks for solving audio classification tasks.