🤖 AI Summary
This study addresses the challenge of detecting and classifying echolocation click signals from marine mammals in complex underwater environments, where low signal-to-noise ratios and reverberation severely degrade performance. To overcome the limitations of conventional short-time Fourier transform–based spectrograms, the authors propose a time–frequency image representation derived from the continuous wavelet transform, which offers improved resolution across both high and low frequency bands. Building on this representation, they introduce CLICK-SPOT, an end-to-end image-based object detection model tailored for click signal identification. Experimental results on a real-world dataset of Norwegian killer whale recordings demonstrate that the proposed approach significantly outperforms traditional spectrogram-based methods, achieving markedly higher accuracy and robustness in detecting and classifying clicks under low signal-to-noise conditions.
📝 Abstract
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