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[2602.17749] Detection and Classification of Cetacean Echolocation Clicks using Image-based Object Detection Methods applied to Advanced Wavelet-based Transformations
Summary
This paper explores the use of advanced wavelet transformations and image-based object detection methods to improve the detection and classification of cetacean echolocation clicks, addressing challenges in marine bioacoustic analysis.
Why It Matters
The study is significant as it presents a novel approach to bioacoustic analysis, which is crucial for understanding cetacean behavior and ecology. By automating the detection process, researchers can analyze larger datasets more efficiently, leading to better insights into marine life and conservation efforts.
Key Takeaways
- Manual labeling of cetacean signals is inefficient; automation is necessary.
- Deep Learning Neural Networks, particularly ANIMAL-SPOT, enhance detection capabilities.
- Wavelet transformations offer improved time and frequency resolution for complex bioacoustic signals.
Electrical Engineering and Systems Science > Audio and Speech Processing arXiv:2602.17749 (eess) [Submitted on 19 Feb 2026] Title:Detection and Classification of Cetacean Echolocation Clicks using Image-based Object Detection Methods applied to Advanced Wavelet-based Transformations Authors:Christopher Hauer View a PDF of the paper titled Detection and Classification of Cetacean Echolocation Clicks using Image-based Object Detection Methods applied to Advanced Wavelet-based Transformations, by Christopher Hauer View PDF HTML (experimental) 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 hi...