165 citations · 307 across the 10 of their papers we have counts for
6 papers · 1 filter
Data augmentation approaches for improving animal audio classification
Loris Nanni, Gianluca Maguolo, Michelangelo Paci
In this paper we present ensembles of classifiers for automated animal audio classification, exploiting different data augmentation techniques for training Convolutional Neural Net…
Audiogmenter: a MATLAB Toolbox for Audio Data Augmentation
Gianluca Maguolo, Michelangelo Paci, Loris Nanni +1
Audio data augmentation is a key step in training deep neural networks for solving audio classification tasks. In this paper, we introduce Audiogmenter, a novel audio data augmenta…
Insect pest image detection and recognition based on bio-inspired methods
Loris Nanni, Gianluca Maguolo, Fabio Pancino
Insect pests recognition is necessary for crop protection in many areas of the world. In this paper we propose an automatic classifier based on the fusion between saliency methods…
Deep learning for Plankton and Coral Classification
Alessandra Lumini, Loris Nanni, Gianluca Maguolo
Oceans are the essential lifeblood of the Earth: they provide over 70% of the oxygen and over 97% of the water. Plankton and corals are two of the most fundamental components of oc…
Ensemble of Convolutional Neural Networks Trained with Different Activation Functions
Gianluca Maguolo, Loris Nanni, Stefano Ghidoni
Activation functions play a vital role in the training of Convolutional Neural Networks. For this reason, to develop efficient and performing functions is a crucial problem in the…
General Purpose (GenP) Bioimage Ensemble of Handcrafted and Learned Features with Data Augmentation
L. Nanni, S. Brahnam, S. Ghidoni +1
Bioimage classification plays a crucial role in many biological problems. In this work, we present a new General Purpose (GenP) ensemble that boosts performance by combining local…