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All Journal Research on Instrumentation
Ei Paing Phyo
Department of Electronic Engineering, Yangon Technological University, Yangon, MYANMAR

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Comparative Analysis of Siren Classification Technique for Emergency Vehicles Ei Paing Phyo; Hla Myo Tun; Thanda Win; Lei Lei Yin Win
Research on Instrumentation Vol. 1 No. 1 (2024): Research on Instrumentation
Publisher : RESSTECH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66926/rins.2024.4

Abstract

Emergency vehicle sirens greatly aid traffic control and public safety awareness. Improving emergency response systems requires accurate siren classification. This study aims to categorize emergency vehicles, particularly fire trucks, police cars, and ambulances, based on the features of their sirens. It thoroughly analyses various schemes for categorizing emergency vehicle sirens. Mel-Frequency Cepstral Coefficients (MFCC), Zero-Crossing Rate (ZCR), Spectral Centroid, and hybrid methods that combine MFCC with ZCR and Spectral Centroid were observed for comparison. The data set is sourced from the Google Audio Set Ontology, ensuring robust training and evaluation of the models. This methodology involves preprocessing audio data, extracting relevant features, and training classifiers. The proposed hybrid method combines MFCC with Spectral Centroid to leverage their complementary strengths. Through rigorous experimentation, this system evaluates the performance of different classifiers, aiming to provide insights for optimal siren classification. The findings contribute to advancing audio classification methodologies and have implications for developing more robust emergency response and traffic management systems.
Co-Authors Hla Myo Tun Lei Lei Yin Win Thanda Win