论文标题
珊瑚礁生物源的深层嵌入聚类
Deep embedded clustering of coral reef bioacoustics
论文作者
论文摘要
深层聚类应用于珊瑚礁音景中未标记的，自动检测到的信号，以区分鱼类脉搏的呼叫和鲸鱼歌曲的片段。深层嵌入式聚类（DEC）使用信号的固定长度功率谱图学习了潜在特征和形成的分类簇。还提取并用高斯混合物模型（GMM）和常规聚类提取精心挑选的光谱和时间特征。 DEC，GMM和常规聚类在模拟的鱼类脉搏调用（FISH）和鲸鱼单元（鲸鱼）的模拟数据集上进行了测试。 GMM和DEC都达到了很高的精度，并用鱼，鲸鱼以及重叠的鱼类和鲸鱼信号确定了簇。常规的聚类方法的精度较低，在具有不等的群集或重叠信号的情况下。 2020年2月3月在夏威夷附近记录的鱼类和鲸鱼信号聚集在DEC，GMM和常规聚类中。 DEC的功能证明，在一个小的，手动标记的数据集中，将信号分类为鱼类和鲸鱼簇的小型数据集中的精度最高77.5％。
Deep clustering was applied to unlabeled, automatically detected signals in a coral reef soundscape to distinguish fish pulse calls from segments of whale song. Deep embedded clustering (DEC) learned latent features and formed classification clusters using fixed-length power spectrograms of the signals. Handpicked spectral and temporal features were also extracted and clustered with Gaussian mixture models (GMM) and conventional clustering. DEC, GMM, and conventional clustering were tested on simulated datasets of fish pulse calls (fish) and whale song units (whale) with randomized bandwidth, duration, and SNR. Both GMM and DEC achieved high accuracy and identified clusters with fish, whale, and overlapping fish and whale signals. Conventional clustering methods had low accuracy in scenarios with unequal-sized clusters or overlapping signals. Fish and whale signals recorded near Hawaii in February-March 2020 were clustered with DEC, GMM, and conventional clustering. DEC features demonstrated the highest accuracy of 77.5% on a small, manually labeled dataset for classifying signals into fish and whale clusters.
