Acoustic signal classification methods of Macrobrachium rosenbergii based on improved residual network

The precise identification of shrimp behavior in aquaculture is of great significance for optimizing feeding and disease prevention. In view of the limitations of traditional optical monitoring methods in complex aquaculture environments, with integrated passive acoustic technology, this research acquires the acoustic information associated with different behaviors of the Macrobrachium rosenbergii and proposes a deep learning-based method for behavior recognition in M. rosenbergii. The acoustic signals of three behaviors (i.e.feeding, moving, and fighting); were converted and converted into Mel spectrograms as the dataset. Then the classification effects of CNN, ResNet18, and VGG16 neural network models were compared. The results showed that ResNet18 in terms of recognition accuracy (97.67%) outperforms VGG16 and CNN. After further introducing the Batch Normalization (BN) algorithm, the recognition accuracy of BN-ResNet18 increased to 99.00%, representing a 1.33% enhancement relative to the baseline ResNet18 model. In addition, BN-ResNet18 showed the best classification performance in the 14.0-44.1 kHz frequency band, which further proved that the synergistic optimization of residual connection and BN module could effectively enhance model performance. BN-ResNet18 demonstrates high accuracy and robustness in feature classification of complex behavioral acoustic signals. This study provides technical support for intelligent recognition based on the acoustic signals of shrimp behaviors and has potential application value in the refined management of aquaculture.