To bee or not to bee: Investigating machine learning approaches for beehive sound recognition

TL;DR

This paper investigates machine learning techniques, including SVMs and CNNs, for beehive sound recognition, emphasizing the importance of data annotation and system development considerations.

Contribution

It introduces a new annotated dataset for beehive sounds and compares machine learning methods for recognition tasks.

Findings

Support vector machines and CNNs both effective for sound recognition

Annotated dataset improves training and evaluation

Key considerations identified for system development

Abstract

In this work, we aim to explore the potential of machine learning methods to the problem of beehive sound recognition. A major contribution of this work is the creation and release of annotations for a selection of beehive recordings. By experimenting with both support vector machines and convolutional neural networks, we explore important aspects to be considered in the development of beehive sound recognition systems using machine learning approaches.