Ensemble of convolutional neural networks to improve animal audio classification

In this work, we present an ensemble for automated audio classification that fuses different types of features extracted from audio files. These features are evaluated, compared, and fused with the goal of producing better classification accuracy than other state-of-the-art approaches without ad hoc parameter optimization. We present an ensemble of classifiers that performs competitively on different types of animal audio datasets using the same...