Automatic identification of animal species by their vocalization is an important and
challenging task. Although many kinds of audio monitoring system have been proposed in …

In-class species classification based on animal sounds is a highly challenging task even
with the latest deep learning technique applied. The difficulty of distinguishing the species is …

Automated classification of calling bird species is useful for large-scale temporal and spatial
environmental monitoring. In this paper, we investigate acoustic features, visual features …

Adopting a deep learning model into bird sound classification tasks becomes a common
practice in order to construct a robust automated bird sound detection system. In this paper …

This paper presents an improved approach to train models that can be used to accurately
predicting animal presence based on its sound with a limited dataset. Currently, deep …

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 …

In this paper we present ensembles of classifiers for automated animal audio classification,
exploiting different data augmentation techniques for training Convolutional Neural …

Animal sound classification (ASC) refers to the automatic identification of animal categories
by sound, and is useful for monitoring rare or elusive wildlife. Thus far, deep-learning-based …

Pattern classification based on deep network outperforms conventional methods in many
tasks. However, if the database for training exhibits internal representation that lacks …

Automatic bird sound classification plays an important role in monitoring and further
protecting biodiversity. Recent advances in acoustic sensor networks and deep learning …