三种鱼类发声信号频带能量特征提取与分类研究

海洋渔业资源的开发和利用对经济鱼类的分类识别提出了迫切的技术需求.根据鱼类的不同发声特征,文章采用有监督机器学习方法实现了三个不同鱼种发声信号的分类.基于小波包分解技术提取了黄花鱼、大米鱼和黄姑鱼三种鱼类发声信号的频带能量特征,并利用不同的分类器进行分类.结果表明:三种鱼的发声信号频率主要集中在300～800 Hz之间...

Research on band energy extraction and classification of three kinds of fishes sound signals

The exploitation and utilization of marine fishery resources put forward urgent technical requirements for classification and recognition of economic fishes. According to the different acoustic features of vocal fishes, the classification of vocal signals of three kinds of fishes is implemented by supervised machine learning in this paper. Based on wavelet packet decomposition technique, the band energies of the vocal signals of yellow croaker, rice fish and yellow drum are extracted and classified by different classifiers. The results show that the vocal signals of the three kinds of fishes are mainly concentrated in the frequency bands of 300-800 Hz, and the band energies of vocal signals based on wavelet packet decomposition can achieve effective classification for the three kinds of fishes. In different classifiers, linear discriminant classifier and random subspace discriminant classifier have better classification results. This method can provide services for the exploitation and utilization of marine fishery resources.