Ibrahim Ali K, Chérubin Laurent M, Zhuang Hanqi, Schärer Umpierre Michelle T, Dalgleish Fraser, Erdol Nurgun, Ouyang B, Dalgleish A
Department Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, Florida 33431, USA.
Harbor Branch Oceanographic Institute, Florida Atlantic University, 5600 US1 North, Fort Pierce, Florida 34946, USA.
J Acoust Soc Am. 2018 Feb;143(2):666. doi: 10.1121/1.5022281.
Grouper, a family of marine fishes, produce distinct vocalizations associated with their reproductive behavior during spawning aggregation. These low frequencies sounds (50-350 Hz) consist of a series of pulses repeated at a variable rate. In this paper, an approach is presented for automatic classification of grouper vocalizations from ambient sounds recorded in situ with fixed hydrophones based on weighted features and sparse classifier. Group sounds were labeled initially by humans for training and testing various feature extraction and classification methods. In the feature extraction phase, four types of features were used to extract features of sounds produced by groupers. Once the sound features were extracted, three types of representative classifiers were applied to categorize the species that produced these sounds. Experimental results showed that the overall percentage of identification using the best combination of the selected feature extractor weighted mel frequency cepstral coefficients and sparse classifier achieved 82.7% accuracy. The proposed algorithm has been implemented in an autonomous platform (wave glider) for real-time detection and classification of group vocalizations.
石斑鱼是一种海洋鱼类，在产卵聚集期间会发出与繁殖行为相关的独特叫声。这些低频声音（50 - 350赫兹）由一系列以可变速率重复的脉冲组成。本文提出了一种基于加权特征和稀疏分类器，对使用固定水听器原位记录的环境声音中的石斑鱼叫声进行自动分类的方法。最初由人工对石斑鱼叫声进行标记，用于训练和测试各种特征提取和分类方法。在特征提取阶段，使用了四种类型的特征来提取石斑鱼发出声音的特征。一旦提取出声音特征，就应用三种代表性分类器对发出这些声音的物种进行分类。实验结果表明，使用所选特征提取器加权梅尔频率倒谱系数和稀疏分类器的最佳组合进行识别的总体准确率达到了82.7%。所提出的算法已在一个自主平台（波浪滑翔器）上实现，用于石斑鱼叫声的实时检测和分类。
