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首页> 外文会议>IEEE International Conference on Machine Learning and Applications >Classifying Humpback Whale Calls to Song and Non-Song Vocalizations using Bag of Words Descriptor on Acoustic Data
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Classifying Humpback Whale Calls to Song and Non-Song Vocalizations using Bag of Words Descriptor on Acoustic Data

机译：使用声学数据词袋描述器对座头鲸的歌声和非歌声进行分类

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摘要

Humpback whale behavior, population distribution and structure can be inferred from long term underwater passive acoustic monitoring of their vocalizations. Here we develop automatic approaches for classifying humpback whale vocalizations into the two categories of song and non-song, employing machine learning techniques. The vocalization behavior of humpback whales was monitored over instantaneous vast areas of the Gulf of Maine using a large aperture coherent hydrophone array system via the passive ocean acoustic waveguide remote sensing technique over multiple diel cycles in Fall 2006. We use wavelet signal denoising and coherent array processing to enhance the signal-to-noise ratio. To build features vector for every time sequence of the beamformed signals, we employ Bag of Words approach to time-frequency features. Finally, we apply Support Vector Machine (SVM), Neural Networks, and Naive Bayes to classify the acoustic data and compare their performances. Best results are obtained using Mel Frequency Cepstrum Coefficient (MFCC) features and SVM which leads to 94% accuracy and 72.73% F1-score for humpback whale song versus non-song vocalization classification, showing effectiveness of the proposed approach for real-time classification at sea.
机译：座头鲸的行为，种群分布和结构可以从对它们的发声的长期水下被动声学监测中推断出来。在这里，我们使用机器学习技术开发了自动方法，将座头鲸的发声分为歌曲和非歌曲两大类。使用大孔径相干水听器阵列系统，通过无源海洋声波导管遥感技术，在2006年秋季的多个diel周期内，通过大口径相干水听器阵列对座头鲸的发声行为进行了监测。我们使用小波信号降噪和相干阵列进行处理以提高信噪比。为了为波束成形信号的每个时间序列建立特征向量，我们采用词袋方法对时频特征进行处理。最后，我们应用支持向量机（SVM），神经网络和朴素贝叶斯对声学数据进行分类并比较其性能。使用梅尔频率倒谱系数（MFCC）功能和SVM可获得最佳结果，与非歌曲发声分类相比，座头鲸歌曲的准确度为94％，F1分数为72.73％，显示了建议的实时分类方法的有效性海。

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