Improved pitch shifting data augmentation for ship-radiated noise classification

Abstract

The limited amount of ship-radiated noise data causes machine learning models to be prone to overfitting in training, and data augmentation methods could improve model generalization performance. The frequency stability of harmonic line spectra in ship-radiated noise leads to a lack of sample diversity generated by the pitch shifting method, which is overcome by the proposed improved method. Nine classification algorithms combining three time-frequency features and three classifiers are implemented and evaluated on the DeepShip and the ShipsEar datasets. The average accuracy increased by 1.67% on DeepShip and 2.25% on ShipsEar, using improved pitch shifting and time stretching augmentation methods. The constant-Q transformation-convolutional neural networks (CQT-CNN) performs best among these nine algorithms. Its accuracy improved from 68.33% to 74.08% on the DeepShip, and the F1 score of it improved from 57.92% to 61.45% on the ShipsEar. Data augmentation improves classification performance for each class of ships in different ways, suggesting that augmentation specific to the class and state of the ship would improve classification performance further.