Reduced MFCC Feature Extraction Dimension for Acoustic Classification of Bee Swarm Activity

This paper proposes an approach, how to speed up the acoustic classification of bee swarm activity. The proposed system could be used as a daily monitoring solution for beehives, especially if they are located remotely. Recorded audio signal was used for acoustic classification with the Mel-frequency cepstral coefficients and hidden Markov acoustic models. The research objective was to analyze the influence of the reduced number of feature extraction coefficients on classification accuracy and real-time factor. Experiments were carried out with the Open Source Beehives Project audio recordings. The baseline system achieved 86,00% classification accuracy. The optimal acoustic classification system with 6 Mel-frequency cepstral coefficients achieved 85.38% accuracy and a 22.1% speed improvement over the baseline system.