Honeybees are the most specialized insect pollinators and are critical not only for honey production but, also, for keeping the environmental balance by pollinating the flowers of a wide variety of crops.

Recording and analyzing bee sounds became a fundamental part of recent initiatives in the development of so-called smart hives. The majority of researches on beehive sound analytics are focusing on swarming detection, a relatively simple binary classification task (due to the obvious difference in the sound of a swarming and a non-swarming bee colony) where machine learning models achieve good performance even when trained on small data.

However, in the case of more complex tasks of beehive sound analytics, even modern machine learning approaches perform poorly. First, training such models would need a large dataset but, according to our knowledge, there is no publicly available large-scale beehive audio data. Second, due to the specifics of beehive sounds, efficient noise filtering methods would be required, however, we could not find a noise filtering method that would increase the performance of machine learning models substantially.

In this paper, we propose a dynamic noise filtering method applicable on spectrograms (image representations of audio data) which is superior to the most popular image noise filtering baselines. Further, we introduce a multi-class classification task of bee sounds and a large-scale dataset consisting of 10.000 beehive audio recordings. Finally, we provide the results of a large-scale experiment involving various combinations of audio feature extraction and noise filtering methods together with various deep learning models. We believe that the contributions of this paper will facilitate further research in the area of (beehive) sound analytics.