Birds are considered critical indicators of ecosystem condition. Automatic recording devices
have emerged as a trending tool to assist field observations, contributing to biodiversity …

Soundscape ecology is a promising area that studies landscape patterns based on their
acoustic composition. It focuses on the distribution of biotic and abiotic sounds at different …

Birds may need to be identified for purposes such as environmental monitoring, follow-up,
and species detection in the ecological area. Automatic sound classifiers have been used to …

Ecoacoustic monitoring has proved to be a viable approach to capture ecological data
related to animal communities. While experts can manually annotate audio samples, the …

A primary challenge in pattern recognition is imbalanced datasets, resulting in skewed and
biased predictions. This problem is exacerbated by limited data availability, increasing the …

The recognition of bird species through the analysis of their vocalizations is a crucial aspect
of wildlife conservation and biodiversity monitoring. In this study, the acoustic features of …

Due to the unique mechanism of bird vocalization, it is difficult to achieve the ideal
recognition accuracy when applying the acoustic characteristics based on human …

Multi-instance (MI) learning is a branch of machine learning, where each object (bag)
consists of multiple feature vectors (instances)—for example, an image consisting of multiple …

Novel bioacoustics signal processing techniques have been developed to classify frog
vocalisations in both trophy and field recordings. The research is useful in helping ecologists …

Ground parrot vocalisation can be considered as an audio event. Test-based diverse density
multiple instance learning (TB-DD-MIL) is proposed for detecting this event in audio files …