Mel倒谱
隐马尔可夫模型
特征提取
计算机科学
语音识别
模式识别(心理学)
特征(语言学)
群体行为
维数(图论)
人工智能
信号(编程语言)
数学
语言学
哲学
程序设计语言
纯数学
标识
DOI:10.1109/elektro53996.2022.9803441
摘要
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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