Automated Detection of Pacific Cod (Gadus macrocephalus) Grunt Signals in Long-Term Acoustic Data Using a Hybrid Matched Filter and ResNet-18 Model

Passive acoustic monitoring (PAM) is a widely adopted methodology for the continuous, long-term observation of underwater marine organisms as well as various underwater noise sources using acoustic signals. However, manually identifying specific signals within long-term monitoring datasets is cumbersome and susceptible to potential biases. This study proposes a hybrid automated detection framework that simultaneously improves detection accuracy and operational efficiency for continuous 45-day underwater acoustic data on the grunt signals of Pacific cod (Gadus macrocephalus). The framework employs a theoretical kernel matching the dominant signal harmonic frequency components to rapidly filter potential candidates with minimal false negatives, followed by a class-weighted ResNet-18 to eliminate residual false positives arising from severe class imbalance in real-sea environments. To prevent data snooping, the classification probability threshold was systematically investigated on an independent validation dataset; freezing the canonical threshold at 0.5 effectively suppressed false alarms caused by ambient soundscapes. On the chronologically distinct test dataset, the proposed model achieved an outstanding and balanced classification performance, with 97.67% precision, 94.83% recall, and a 96.23% F1-score. These findings demonstrate that the integrated matched filter and deep learning pipeline provides a reliable automated approach for identifying biological fish sounds within complex marine acoustic environments.