Acoustic monitoring and classification of bee swarm activity using MFCC feature extraction and HMM acoustic modeling

This paper presents a proposed system for acoustic monitoring and classification of bee activity, based on audio modality. Beekeeping is one of the agriculture sectors, where ICT solutions provide great benefit for production and animal welfare. The presented system is a general classifier, which can classify normal and swarm bee activity. The captured audio signal is transformed into mel-frequency cepstral coefficients, which are used to train one state hidden Markov models. The activity classification is carried out in a form of recognition architecture, based on principles applied to speech modality. The acoustic models were trained in a two-stage approach using the open audio data provided by the Open Source Beehives Project. The highest classification accuracy of 80.89% was achieved with the proposed general classification approach.