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 intersong 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.INDEX TERMS audio aesthetics, behavioral biometrics, biometric security, human-machine interactions, pattern recognition

A Novel Three Stage Framework for Person Identification From Audio Aesthetic

“…The user count in FMA dataset is 34, while for MPD dataset it is 200. The audio aesthetic system introduced by Sieu and Gavrilova [5] achieved a rank 1 accuracy of 95.74% on the FMA dataset and 99.70% accuracy on the MPD dataset. Furthermore, in their work, the inference time for FMA and MPD datasets was 1.85s and 8.12s, respectively.…”

“…The very first audio aesthetic system was developed in 2021 and achieved a significant accuracy by extracting handcrafted audio features from user liked-songs [5]. This research demonstrated that, similar to visual aesthetics, audio aesthetic features have unique and distinguishing characteristics for biometric identification.…”

“…Prior works have established a correlation between human personality and audio preferences. The first proof of concept research in this domain in 2021 [5]. Authors developed the first audio aesthetic-based person identification system that achieved 95% user recognition accuracy on FMA and MPD datasets.…”

“…The performance of the proposed three-stage audio aesthetic framework was evaluated on two datasets; one is proprietory [5], and another is publicly available [42]. The proprietary dataset consists of 34 users and their corresponding ten favorite songs.…”

“…Furthermore, studies of cognitive neuroscience have emphasized that there is a significant link between human perceptions of aesthetics and personality, making personal aesthetics one of the prominent and desirable traits for biometric authentication [4]. Aesthetic biometric systems have potential to transform many existing applications, such as multi-factor authentication, human behavioral analysis, and recommender systems [5].…”