Detection and Classification of Cetacean Echolocation Clicks using Image-based Object Detection Methods applied to Advanced Wavelet-based Transformations
arXiv:2602.17749v1 Announce Type: cross 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 freq
arXiv:2602.17749v1 Announce Type: cross 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
Executive Summary
This article explores the application of image-based object detection methods using advanced wavelet-based transformations for the detection and classification of cetacean echolocation clicks. The proposed approach, CLICK-SPOT, leverages deep learning neural networks to process audio signals as image representations, addressing the limitations of traditional spectrogram-based methods. The article demonstrates the efficacy of CLICK-SPOT on Norwegian Killer whale underwater recordings, highlighting its potential for efficient and accurate analysis of complex bioacoustic data.
Key Points
- ▸ Application of image-based object detection methods for cetacean echolocation click detection
- ▸ Use of wavelet-based transformations to improve feature extraction in complex bioacoustic environments
- ▸ Evaluation of CLICK-SPOT on Norwegian Killer whale underwater recordings
Merits
Improved Time-Frequency Resolution
The wavelet-based transformation approach provides better time resolution for high frequencies and improved frequency resolution for low frequencies, allowing for more accurate feature extraction.
Demerits
Computational Complexity
The use of deep learning neural networks and wavelet-based transformations may increase computational complexity, potentially limiting the applicability of CLICK-SPOT for real-time analysis.
Expert Commentary
The article presents a compelling approach to addressing the challenges of cetacean echolocation click detection, leveraging the strengths of deep learning neural networks and wavelet-based transformations. The use of image-based object detection methods is a novel and promising direction, and the evaluation of CLICK-SPOT on real-world data demonstrates its potential for practical application. However, further research is needed to fully explore the limitations and potential extensions of this approach, including its scalability and robustness in diverse bioacoustic environments.
Recommendations
- ✓ Further evaluation of CLICK-SPOT on diverse bioacoustic datasets to assess its robustness and generalizability
- ✓ Investigation of potential applications of CLICK-SPOT in related fields, such as environmental monitoring and conservation biology