凡纳滨对虾摄食不同饵料的声音信号分类模型研究

Study on classification models for acoustic signals of Litopenaeus vannamei feeding on different kinds of diets

  • 摘要: 利用机器学习技术对凡纳滨对虾 (Litopenaeus vannamei) 摄食不同饵料时的声音信号进行分类,旨在比较不同分类模型的性能,确定最优模型,为对虾养殖中饵料管理的信息化提供参考。研究选取对虾摄食沙蚕、颗粒饲料和鱿鱼时的声音信号,经降噪滤波处理后,通过两类方式分类:1) 基于音频特征向量,分别建立支持向量机 (SVM)、随机森林 (RF) 和K-最近邻 (KNN) 模型;2) 基于梅尔频谱图,建立卷积神经网络模型 (CNN)。结果表明,结合Mixup数据增强技术和粒子群优化算法 (PSO) 的CNN模型在准确率方面表现最佳,达到91.67%。4个模型在识别颗粒饲料的召回率均超过90%,说明摄食颗粒饲料的声音信号相较于摄食沙蚕和鱿鱼等软体饵料更易识别。CNN模型不仅在准确率、精确度、召回率和F1分数等指标上均优于上述传统模型,同时能够适应复杂声学信号的分析需求,具有较大的应用潜力。

     

    Abstract: We explored the classification of acoustic signals of L. vannamei feeding on different kinds of diets with machine learning techniques, so as to recognize the best model by comparing the performance among different classification models, providing references for the informatization of feed management in shrimp aquaculture. We selected and processed the acoustic signals of L. vannamei feeding on nereid, pellet feed and squid by using noise reduction and filtering. Two classification ways were employed: 1) Building models including Support vector machine (SVM), Random forest (RF), and k-nearest neighbor (KNN) based on audio feature vectors; 2) Building Convolutional neural network (CNN) based on Mel-spectrograms. The results indicate that the CNN model, enhanced with Mixup data augmentation and Particle Swarm Optimization (PSO), achieved the highest accuracy of 91.67%. In addition, all four models achieved a recall rate exceeding 90% in identifying pellets, which indicates that the feeding acoustic signals of shrimps consuming pellets were more distinguishable than those associated with nereid and squid. The CNN model outperformed the traditional models in accuracy, precision, recall, and F1 score, exhibiting greater adaptability for analyzing complex acoustic signals with significant potential for its practical applications.

     

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