Person Identification From Audio Aesthetic

Abstract

Behavioral biometrics survey actions rather than the physical traits of the person. Within this categorization, social behavioral biometrics utilizes an individual's communications for biometric analysis. The investigation of the uniqueness of human preferences and their implications to other aspects of an individual, such as personality or gender, is both a psychological and a biometric problem. An emerging approach is the usage of an individual's aesthetic preferences for the purpose of person identification. Recent research into the identification from visual aesthetics has found that these preferences hold significant discriminatory value. However, aesthetic identification has only been conducted through a visual medium via a set of liked images. The contribution of this work is the development of the first audio aesthetic preference system for person identification. The proposed system extracts descriptive intra-song and inter-song features from a set of songs favored by users and utilizes an ensemble of classifiers for prediction. The final decision is optimized by a genetic algorithm. Experimental results demonstrate that the developed audio aesthetic system achieves 95% user recognition accuracy on both proprietary and public audio datasets.