Classification of audio samples by convolutional networks in audio beehive monitoring | Vestnik Tomskogo gosudarstvennogo universiteta. Upravlenie, vychislitelnaja tehnika i informatika – Tomsk State University Journal of Control and Computer Science. 2018. № 45. DOI: 10.17223/19988605/45/8

Classification of audio samples by convolutional networks in audio beehive monitoring

In the investigation, we consider the problem of classification of audio samples resulting from the audio beehive monitoring. Audio beehive monitoring is a key component of electronic beehive monitoring (EBM) that can potentially automate the identification of various stressors for honeybee colonies. We propose to use convolutional neural networks (ConvNets) and compare developed ConvNets in classifying audio samples from electronic beehive monitors deployed in live beehives. As a result, samples are placed in one of the three non-overlapping categories: bee buzzing (B), cricket chirping (C), and ambient noise (N). We show that ConvNets trained to classify raw audio samples perform slightly better than ConvNets trained to classify spectrogram images of audio samples. We demonstrate that ConvNets can successfully operate in situ on low voltage devices such as the credit card size raspberry pi computer.

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Keywords

глубокое обучение, сверточные нейронные сети, классификация аудио, обработка аудио, электронный мониторинг пчелиных ульев, deep learning, machine learning, convolutional neural networks, audio classification, audio processing, electronic beehive monitoring

Authors

Name	Organization	E-mail
Kulyukin Vladimir Alekseevich	Utah State University	vladimir.kulyukin@usu.edu
Mukherjee Sarbajit	Utah State University	mukherjee@aggiemail.usu.edu
Burkatovskaya Yulia Borisovna	Tomsk Polytechnic University	tracey@tpu.ru

Всего: 3

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