Hearing the Ocean: Bio-inspired Gammatone-CNN framework for Robust Underwater Acoustic Target Classification
Abstract
This study presents a bio inspired signal processing framework for robust Underwater Acoustic Target Recognition (UATR). The latest state of the art methods often fail to resolve dense low frequency harmonic structures in vessel propulsion signals under high noise conditions, which is addressed by the proposed framework using a biologically inspired Gammatone filter bank that emulates the cochlea nonlinear frequency selectivity. By distributing filters according to the Equivalent Rectangular Bandwidth (ERB) scale, the framework achieves a high fidelity representation of engine radiated tonals while effectively suppressing isotropic ambient interference. The resulting Cochleagram features are processed by a lightweight, custom designed Convolutional Neural Network (CNN) that leverages large receptive fields to integrate spectral-temporal continuities. Experimental results on the VTUAD dataset demonstrate a state of the art classification accuracy of 98.41%, outperforming Continuous Wavelet Transform and Mel Frequency Cepstral Coefficients baselines by 3.5% and 7.7% respectively. Furthermore, the framework achieves an inference latency of only 0.77 ms and a 0.971 Cohen Kappa score, validating its efficacy for real time deployment on autonomous, low-power sonar hardware.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the development of a bio-inspired Gammatone-CNN framework for underwater acoustic target classification, achieving state-of-the-art performance through innovative feature extraction techniques. This research significantly advances the field of underwater acoustics by providing a method that combines biological principles with modern machine learning, demonstrating the potential for improved classification accuracy in challenging acoustic environments.
Methodology
The paper introduces a novel bio-inspired Gammatone filter bank for underwater acoustic target classification, leveraging the non-linear frequency selectivity of the cochlea. The methodology prioritizes feature extraction over architectural complexity, employing a lightweight CNN that effectively integrates spectral-temporal features. The use of the Equivalent Rectangular Bandwidth (ERB) scale for filter distribution is particularly innovative, allowing for high fidelity in low-frequency representation, which is crucial for underwater acoustics. The mathematical foundations of the Gammatone filter and the detailed description of the Cochleagram formation process provide a solid basis for the proposed approach.
Results & evaluation
The experimental validation is robust, utilizing the VTUAD dataset to demonstrate the framework's effectiveness. Achieving a classification accuracy of 98.41% and a Cohen Kappa score of 0.971 indicates strong performance and reliability. The comparative analysis against established methods like Continuous Wavelet Transform and Mel Frequency Cepstral Coefficients shows significant improvements, reinforcing the proposed method's superiority. The inclusion of diverse metrics such as ROC curves and confusion matrices adds depth to the evaluation.
Reproducibility
The paper provides detailed information on the experimental setup, including dataset partitioning, feature extraction parameters, and model architecture. However, the absence of a publicly available code repository limits reproducibility. Future work should consider sharing implementation details to facilitate validation by other researchers.
Limitations
While the proposed framework shows remarkable performance, it may struggle with class imbalance, particularly for underrepresented classes like Passengership. The reliance on a specific dataset (VTUAD) may also limit generalizability to other underwater environments. Additionally, the computational efficiency on standard CPUs, while acceptable, could be a concern for real-time applications in more constrained environments.
Broader impact
The implications of this research extend to maritime security, ecological monitoring, and autonomous underwater vehicles (AUVs). By improving underwater target recognition, the framework can enhance surveillance capabilities and contribute to the protection of marine ecosystems. The low-power, real-time processing capabilities make it suitable for deployment in resource-constrained environments. The main contribution of this paper is the development of a bio-inspired Gammatone-CNN framework for underwater acoustic target classification, achieving state-of-the-art performance through innovative feature extraction techniques. This research significantly advances the field of underwater acoustics by providing a method that combines biological principles with modern machine learning, demonstrating the potential for improved classification accuracy in challenging acoustic environments.
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