Acoustic Classification of Maritime Vessels using Learnable Filterbanks
This work addresses the challenge of robust acoustic monitoring for maritime security or environmental applications, representing a strong domain-specific advancement.
The paper tackles the problem of classifying maritime vessels from acoustic signatures across diverse recording scenarios, achieving a state-of-the-art 96.63% test accuracy, which surpasses the previous benchmark by over 12 percentage points.
Reliably monitoring and recognizing maritime vessels based on acoustic signatures is complicated by the variability of different recording scenarios. A robust classification framework must be able to generalize across diverse acoustic environments and variable source-sensor distances. To this end, we present a deep learning model with robust performance across different recording scenarios. Using a trainable spectral front-end and temporal feature encoder to learn a Gabor filterbank, the model can dynamically emphasize different frequency components. Trained on the VTUAD hydrophone recordings from the Strait of Georgia, our model, CATFISH, achieves a state-of-the-art 96.63 % percent test accuracy across varying source-sensor distances, surpassing the previous benchmark by over 12 percentage points. We present the model, justify our architectural choices, analyze the learned Gabor filters, and perform ablation studies on sensor data fusion and attention-based pooling.