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

翻译：海洋生物声学分析中的一个挑战在于检测动物信号（如叫声、哨声和点击声）以进行行为研究。手动标记过于耗时，难以处理足够数据以获得可靠结果。因此，需要一种自动化解决方案来克服耗时的数据分析问题。基础数学模型可在简单环境中检测事件，但在复杂场景中表现不佳，例如难以区分低信噪比信号或辨别点击声与回声。深度学习神经网络（如ANIMAL-SPOT）更适合此类任务。深度神经网络将音频信号作为图像表示进行处理，通常使用短时傅里叶变换生成的声谱图。然而，受不确定性原理限制，声谱图在时间与频率分辨率之间存在固有权衡。小波变换等替代方法可为高频提供更优时间分辨率，为低频提供更佳频率分辨率，可能在复杂生物声学环境中为特征提取带来优势。本论文通过鲸类生物学家Vester博士提供的挪威虎鲸水下录音数据，验证了CLICK-SPOT系统的有效性。关键词：生物声学，深度学习，小波变换