Intelligent Classification of Automotive Horn Sound Quality

Abstract

<div class="section abstract"><div class="htmlview paragraph">The primary function of an automotive horn is to alert pedestrians and other nearby vehicles to their safe passage on the road. Most of the human population is subjected to a certain amount of horn sound dosage daily. The study of automotive horn sound quality is equally important as their sound generation mechanism in passenger cars. The sound quality of automotive horns can be studied through subjective and objective test methods. In the present study, a subjective jury test and objective analysis using psychoacoustic parameters are conducted to classify car horn sound samples according to pleasantness. Twenty-two car horns, consisting of a disc and shell, are chosen for binaural sound recording. The recorded sound samples are used for subjective and objective analysis. Thirty members participated in the jury test, and a semantic differential method was used to collect the user response. The Tukey range test is used to classify the subjective test data. Six parameters, namely SPL (dBA), loudness, sharpness, roughness, fluctuation strength, and tonality, are obtained from the recorded sound samples of automotive horns using the Artemis suite and in-house-developed code for objective analysis. Mahalanobi's distance method and AI/ML-based intelligent classification techniques were further used to group the horn sounds using calculated objective parameters. Critical psychoacoustic parameters with the highest weightage on the classification of horn sound samples were identified and AI/ML models were reconstructed. Reconstructed models produce similar classification accuracy as compared to the original AI models. Horn sound classifications based on subjective tests, objective analysis and intelligent classification techniques are in good agreement. Developed models can be used to select a pleasant class of automotive horn for final deployment.</div></div>