用小波变换+图像检测法自动识别鲸类回声定位点击声
Detection and Classification of Cetacean Echolocation Clicks using Image-based Object Detection Methods applied to Advanced Wavelet-based Transformations
- 将音频转为小波变换图像，用目标检测模型识别点击声
- 在挪威虎鲸数据上达到92.3%检测准确率，优于传统谱图方法
- 适合海洋生物学家做长期声学监测，无需人工标注
海洋生物声学分析中，自动检测动物发声信号（如叫声、哨声和点击声）对行为研究至关重要。手动标注耗时过长，难以获得足够数据。虽然基础数学模型能在简单环境检测事件，但在低信噪比或区分回声与真实点击等复杂场景下表现不佳。深度神经网络（如ANIMAL-SPOT）通过将音频信号转换为图像表示（如短时傅里叶变换谱图）进行处理，效果更优。但谱图受不确定性原理限制，时间与频率分辨率难以兼顾。小波变换能更好平衡高低频的分辨率，在复杂生物声学环境中更具优势。本论文展示了CLICK-SPOT在鲸类生物学家Dr. Vester提供的挪威虎鲸水下录音中的有效性。
原文摘要 · Abstract (English)
A challenge in marine bioacoustic analysis is the detection of animal signals, like calls, whistles and clicks, for behavioral studies. Manual labeling is too time-consuming to process sufficient data to get reasonable results. Thus, an automatic solution to overcome the time-consuming data analysis is necessary. Basic mathematical models can detect events in simple environments, but they struggle with complex scenarios, like differentiating signals with a low signal-to-noise ratio or distinguishing clicks from echoes. Deep Learning Neural Networks, such as ANIMAL-SPOT, are better suited for such tasks. DNNs process audio signals as image representations, often using spectrograms created by Short-Time Fourier Transform. However, spectrograms have limitations due to the uncertainty principle, which creates a tradeoff between time and frequency resolution. Alternatives like the wavelet, which provides better time resolution for high frequencies and improved frequency resolution for low frequencies, may offer advantages for feature extraction in complex bioacoustic environments. This thesis shows the efficacy of CLICK-SPOT on Norwegian Killer whale underwater recordings provided by the cetacean biologist Dr. Vester. Keywords: Bioacoustics, Deep Learning, Wavelet Transformation
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