Semi-supervised Classifying of Modelled Auditory Nerve Patterns for Vowel Stimuli with Additive Noise
The paper proposes an approach to stationary patterns of auditory neural activity analysis from the point of semi-supervised learning in self-organizing maps (SOM). The suggested approach has allowed to classify and identify complex auditory stimuli, such as vowels, given limited prior information about the data. A computational model of the auditory periphery has been used to obtain auditory nerve fiber responses. Label propagation through Delaunay triangulation proximity graph, derived by SOM algorithm, is implemented to classify unlabeled units. In order to avoid the “dead” unit problem in Emergent SOM and to improve method effectiveness, an adaptive conscience mechanism has been realized. The study has considered the influence of AWGN on the robustness of auditory stimuli identification under various SNRs. The representation of acoustic signals in the form of neural activity in the auditory nerve fibers has proven more noise-robust compared to that in the form of the most common acoustic features, such as MFCC and PLP. The approach has produced high accuracy, both in case of similar sounds and with high SNR.
KeywordsAuditory nerve data analysis Unsupervised learning Neurogram Machine hearing Label propagation Self-organizing maps
The reported study was funded by the Russian Foundation for Basic Research according to the research project 18-31-00304.
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