Ship-Radiated Noise Analysis Based on the Gammatone Frequency Cepstrum Coefficient

Acoustic feature extraction of ship-radiated noise has a major effect on target training and recognition. This research proposes a feature analysis method based on the gammatone frequency cepstrum coefficient(GFCC). The method uses the typical target feature extraction method——Mel frequency cepstrum coefficient(MFCC) algorithm for comparison and uses 5 122 samples with three types of targets, namely, small low-speed, small high-speed, and large vessels. The samples are extracted in two different underwater acoustic environments to generate a classification identification comparison test. Results show that the recognition rate of the two algorithms is greater than 80%. However, the recognition rate of GFCC in an ocean complex acoustic environment is found to be significantly higher than that of MFCC and is more sensitive to high-frequency targets. These results show that the GFCC algorithm has better noise resistance and a higher recognition rate for fast targets in oceanic and other strong interference environments as compared with the standard MFCC algorithm.