Implementation of Bicepstral Target Classifier With Support Vector Machines for Noise Sources in the Ocean

Higher order spectral analysis is becoming one of the key areas in marine acoustic signal processing, especially for the classification and identification of noise generating mechanisms. This paper investigates the feasibility of a target classifier with bicepstral features. Bicepstral coefficients are extracted from the noise data waveform after framing and windowing operations. These features are then used to train a support vector machine classifier. Various kernels are analyzed for the performance measure. It is found that SVM with a radial basis function kernel could achieve a success rate of 80.5%. The kernel parameters were chosen after performing grid search and cross validation. The result indicates that the bicepstral features could be utilized efficiently to form feature sets that could greatly aid in the classification process.