基于谱熵、K均值聚类和连续小波变换的鲸类发声软输出信号检测
Soft-Output Signal Detection for Cetacean Vocalizations Using Spectral Entropy, K-Means Clustering and the Continuous Wavelet Transform
摘要：
水下声学监测系统为海洋研究记录许多小时的音频数据，使得快速可靠的非因果信号检测至关重要。这样的检测器有助于减少信号注释所需的劳动量，信号注释通常包含没有信号的大部分。研究了基于光谱熵的鲸类发声检测方法。以往的谱熵（SE）技术大多考虑熵测度的时频增强，并利用STFT作为其时频（TF）分解。SE方法还需要用户手动设置检测阈值，这需要了解所产生的熵度量。本文认为中值滤波是一种简单、有效的方法，可以为熵测度提供时间稳定性，并将连续小波变换视为一种可供选择的TF分解。K均值聚类用于确定准确分离信号/无信号熵测量所需的阈值，从而导致一维两类分类问题。类均值用于执行伪概率软类分配，这是算法开发中的有用度量。研究了中值滤波、信噪比和所选TF分解的效果。所提出的方法在检测准确性和特异性方面显示出显着的改进，同时还通过软类分配提供了更可解释的检测阈值设置。
英文摘要：
Underwater acoustic monitoring systems record many hours of audio data for marine research, making fast and reliable non-causal signal detection paramount. Such detectors assist in reducing the amount of labor required for signal annotations, which often contain large portions devoid of signals. Cetacean vocalization detection based on spectral entropy is investigated as a means of vocalization discovery. Previous techniques using spectral entropy (SE) mostly consider time-frequency enhancement of the entropy measure, and utilize the STFT as its time-frequency (TF) decomposition. SE methods also requires the user to set a detection threshold manually, which call for knowledge of the produced entropy measures. This paper considers median filtering as a simple, effective way to provide temporal stabilization to the entropy measure, and considers the CWT as an alternative TF decomposition. K-means clustering is used to determine the threshold required to accurately separate the signal/no-signal entropy measures, resulting in a one-dimensional, two-class classification problem. The class means are used to perform pseudo-probabilistic soft class assignment, which is a useful metric in algorithmic development. The effect of median filtering, signal-to-noise ratio and the chosen TF decomposition are investigated. The proposed method shows a significant improvement in detection accuracy and specificity, while also providing a more interpretable detection threshold setting via soft class assignment.
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