Multi-class acoustic event classification of hydrophone data

In this paper, we address the problem of multi-class classification of hydrophone data for acoustic events using low-dimensional features. A new iterative multiclass classification scheme is proposed based on the combination of adaptive MFCC feature set and an improved HMM-GMM classifier. The adaptive window length for MFCC is important since for acoustic sounds in the ocean, the optimum window length may be different unlike the window length of 16 - 32 msec, which is optimum for speech signals. Further, in order to increase the classification performance, we perform the B-spline approximation to the generated Gaussians parameters of the multi model HMM-GMM classifier to enhance the separation of the decision region. Experimental results for the real recorded hydrophone data show that our improved iterative scheme efficiently classifies the acoustic events with high mean accuracy (96%), sensitivity (95%), and specificity (97%).