Under the complex oceanic environment, robust and effective feature extraction is the key issue of ship radiated noise recognition.\nSince traditional feature extraction methods are susceptible to the inevitable environmental noise, the type of vessels, and the\nspeed of ships, the recognition accuracy will degrade significantly. Hence, we propose a robust time-frequency analysis method\nwhich combines resonance-based sparse signal decomposition (RSSD) and Hilbert marginal spectrum (HMS) analysis. First, the\nobserved signals are decomposed into high resonance component, low resonance component, and residual component by RSSD,\nwhich is a nonlinear signal analysis method based not on frequency or scale but on resonance. High resonance component is\nmultiple simultaneous sustained oscillations, low resonance component is nonoscillatory transients, and residual component is\nwhite Gaussian noises. According to the low-frequency periodic oscillatory characteristic of ship radiated noise, high resonance\ncomponent is the purified ship radiated noise. RSSD is suited to noise suppression for low-frequency oscillation signals. Second,\nHMS of high resonance component is extracted by Hilbert-Huang transform (HHT) as the feature vector. Finally, support vector\nmachine (SVM) is adopted as a classifier. Real audio recordings are employed in the experiments under different signal-to-noise\nratios (SNRs).The experimental results indicate that the proposed method has a better recognition performance than the traditional\nmethod under different SNRs.
Loading....