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All Journal Prisma Sains: Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram
Buan Anshari
Universitas Mat
Biochemistry, Genetics & Molecular Biology Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Mathematics Physics Other

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Ambulance Siren Audio Classification Using Convolutional Neural Network for Medical Emergency Detection Ramadhan Paninggalih; Bima Prihasto; Maryo Inri Pratama; Rizky Irswanda Ramadhana; Misbahuddin Misbahuddin; Buan Anshari; Lalu Ahmad Syamsul Irfan Akbar; Giri Wahyu Wiriasto
Prisma Sains : Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram Vol. 14 No. 2: April 2026
Publisher : Universitas Pendidikan Mandalika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/j-ps.v14i2.20099

Abstract

The rapid detection of emergency vehicle sirens is critical for enhancing road safety and traffic management. This study proposes an automated classification system for ambulance sirens using a Convolutional Neural Network (CNN). The method utilizes Mel-Frequency Cepstral Coefficients (MFCC) to transform audio signals into 2D feature maps, allowing the model to capture distinct spectral and temporal patterns. The dataset was preprocessed using a stratified split to ensure balanced class distribution and prevent data leakage. Experimental results demonstrate that the CNN model achieves a high performance with an accuracy of 0.95, significantly outperforming baseline models such as Multi-Layer Perceptron (MLP) and XGBoost. Detailed evaluation through a confusion matrix indicates a consistent precision, recall, and F1-score of 0.95, proving the model’s robustness in distinguishing sirens from complex urban noise. The implementation of the Adam optimizer and early stopping mechanism ensured stable convergence and prevented overfitting. These findings suggest that the proposed CNN-MFCC framework provides a reliable solution for real-time emergency signal detection, offering a substantial contribution to intelligent transportation systems.
Co-Authors Bima Prihasto Giri Wahyu Wiriasto Lalu Ahmad Syamsul Irfan Akbar Pratama, Maryo Inri Ramadhan Paninggalih Rizky Irswanda Ramadhana