VFR: The Underwater Acoustic Target Recognition Using Cross-Domain Pre-Training with FBank Fusion Features

Abstract

Underwater acoustic target recognition is a hot research area in acoustic signal processing. With the development of deep learning, feature extraction and neural network computation have become two major steps of recognition. Due to the complexity of the marine environment, traditional feature extraction cannot express the characteristics of the targets well. In this paper, we propose an underwater acoustic target recognition approach named VFR. VFR adopts a novel feature extraction method by fusing three-dimensional FBank features, and inputs the extracted features into a residual network, instead of the classical CNN network, plus cross-domain pre-training to perform target recognition. The experimental results show that VFR achieves 98.5% recognition accuracy on the randomly divided ShipsEar dataset and 93.8% on the time-divided dataset, respectively, which are better than state-of-the-art results.

A Bird Vocalization Classification Method Based on Improved Adaptive Wavelet Threshold Denoising and Bidirectional FBank

“…Liao et al utilized FBank in a deep neural network (DNN) for giant panda vocalizations 27 , while Lin and Wang applied FBank and Linear FBank for human voiceprint recognition 28 . Wu et al combined FBank, delta, delta-delta FBank, and ResNet18 for underwater target identification 29 .…”