AudioProtoPNet: An interpretable deep learning model for bird sound classification

arXiv:2404.10420v1 Announce Type: new
Abstract: Recently, scientists have proposed several deep learning models to monitor the diversity of bird species. These models can detect bird species with high accuracy by analyzing acoustic signals. However, traditional deep learning algorithms are black-box models that provide no insight into their decision-making process. For domain experts, such as ornithologists, it is crucial that these models are not only efficient, but also interpretable in order to be used as assistive tools. In this study, we …

abstract accuracy algorithms arxiv bird box classification cs.lg decision deep learning deep learning algorithms diversity however insight making process scientists sound species type