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

A. Zgank
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Abstract

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.
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减少MFCC特征提取维数用于蜂群活动声学分类
本文提出了一种加快蜂群活动声学分类的方法。提出的系统可以作为蜂窝的日常监测解决方案,特别是如果它们位于远程位置。利用mel频率倒谱系数和隐马尔可夫声学模型对录制的音频信号进行声学分类。研究目的是分析特征提取系数减少后对分类精度和实时性的影响。实验是用开源蜂箱项目的录音进行的。基线系统达到了86.00%的分类准确率。与基线系统相比,具有6个mel频率倒谱系数的最优声学分类系统的准确率达到85.38%,速度提高22.1%。
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