Robust underwater noise targets classification using auditory inspired time-frequency analysis

Underwater noise targets classification has variable applications in many fields. During the long range detection, inevitable environmental noise will decrease the recognition accuracy. Thus, robust classification methods need to be developed. Inspired by human auditory perception, a time-frequency analysis method that combines the Bark-wavelet analysis and Hilbert-Huang transform is presented. By using Bark-wavelet analysis, signals are divided into different sub-bands that correspond to the auditory perception. Then denoising is applied to enhance the analyzed signals. With the help of Hilbert-Huang transform, instantaneous frequencies and amplitudes are extracted. Based on these instantaneous parameters, various features are constructed and compared. Support vector machines are used as the classifier. Recorded underwater noise targets signals are used for the experiments. Various signal-to-noise ratios are simulated through the adding of white Gaussian noise at various levels. Cross-validation procedure was used in the experiments. The results showed that proposed method could achieve better recognition performances under different SNRs comparing to other methods.