Abstract
The movements and intentions of other ships can be determined by gathering and examining ship sound signals.The extraction and analysis of ship sound signals fundamentally support the autonomous navigation of intelligent ships.Mel scale frequency cepstral coefficient(MFCC)feature parameters are improved and optimized to form NewMFCC by introducing second-order difference and wavelet packet decomposition transformation methods in this paper.Transforming sound signals into a feature vector that fully describes the dynamic characteristics of ship sound signals and the high-and low-frequency information solves the problem of the inability to transport ordinary sound signals directly as signals for training in machine learning models.Radial basis function kernels are used to conduct support vector machine classifier simulation experiments.Five types of sound signals,namely,one type of ship sound signals and four types of interference sound signals,are categorized and identified as classification targets to verify the feasibility of the classification of ship sound signals and interference signals.The proposed method improves classification accuracy by approximately 15％.
NETL
NSTL
万方数据