Modelling and Analysis of Transient Evoked Otoacoustic Emissions for Human Biometric Recognition

Abstract

This paper reports on application of data analytics techniques for determining the status of human ear, variation in ear conditions and the specifics of whose ear they are. Principal component analysis of transient evoked otoacoustic emissions from human ears were derived based on recording of responses to external audio excitations undertaken fifteen months apart. Results indicate the method established in the paper is suitable for person recognition and for identifying when deterioration of the hearing performance of an ear has taken place. Ear transformation matrix is introduced. The transformation matrix represents a scaling of the eigenvectors using a Hermitian matrix or scaling matrix. They are however known to be the eigenvalues obtained from the PCA analysis. While the eigenvalues could be seen to represent audio loudness scaling, the eigenvectors represent further ear deterioration. Eigenvalues are maintained when the audio performance of ear to external excitation has not changed. Variation of the Hermitian is also variation of the ear condition. The lengths of the eigenvectors are considered as estimates of the change in ear loudness performance as it can be seen as equivalent to the power content of the eigenvectors.