A Hybrid Learning Approach for Adaptive Classification of Acoustic Signals Using the Simulated Responses of Auditory Nerve Fibers

In this paper a framework of the heterogeneous system for an adaptive classification of multivariate numerical data is proposed. Considered data characterize the dynamics of the simulated responses of auditory nerve fibers as probability that represents the average of the ensemble for acoustic signals. The possibility to classify sound stimuli by means of analysis of the responses of the auditory nerve for the fibers with high and low spontaneous rates is examined. The aim of the study was to develop and implement a method for an adaptive pattern recognition in limited a priori information about their number and structure. The proposed model architecture consists of several units that generalize basic stages of a perceptual process that in turn corresponds neuro-symbolic information processing approach to the machine perception problem. Proposed method combines the advantages of the self-organizing maps and the radial basic function networks. This combination leads to a hybrid learning approach, which allow to provide the automatic classification of unlabeled data. According to the obtained results, the proposed approach showed better accuracy for several complex benchmark tests as well as for pure tones recognition by means of simulated auditory nerve fibers responses compared to k-means and single-linkage unsupervised classification strategies.