ISSN :2582-9793

Acoustic Spectral Analysis for Emergency Vehicle Detection: Symbolic and CNN Approaches

Original Research (Published On: 19-Apr-2024 )
Acoustic Spectral Analysis for Emergency Vehicle Detection: Symbolic and CNN Approaches

Alberto Pacheco, Raymundo Torres and Raúl Chacón

Adv. Artif. Intell. Mach. Learn., 4 (2):2189-2200

Alberto Pacheco : TecNM campus Chihuahua

Raymundo Torres : TecNM

Raúl Chacón : TecNM

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Article History: Received on: 12-Feb-24, Accepted on: 23-Mar-24, Published on: 19-Apr-24

Corresponding Author: Alberto Pacheco

Email: alberto.pg@chihuahua.tecnm.mx

Citation: Alberto Pacheco, Mariano Rivera, Raymundo Torres. (2024). Acoustic Spectral Analysis for Emergency Vehicle Detection: Symbolic and CNN Approaches. Adv. Artif. Intell. Mach. Learn., 4 (2 ):2189-2200


Abstract

    

During emergencies, ambulances on city streets face delays due to traffic obstacles. This paper addresses two efficient emergency vehicle detection (EVD) methods for restricted hardware implementation considering noisy conditions: a symbolic processing-based algorithm and a convolutional neural network (CNN) model, both of which utilize Mel spectrogram representations of Hi-Lo siren audio records. The symbolic method employs regular expressions and acceptance criteria to process text-pattern features extracted from spectrograms, offering a self-explanatory, easily tunable, and resource-efficient solution suitable for low-cost hardware platforms. On the other hand, the CNN model directly processes spectrogram representations, leveraging spatial correlation for classification with a streamlined architecture consisting of very few layers. The experimental results demonstrate that both approaches achieve high accuracy (97-98\%) in classifying Hi-Lo sirens, with the CNN model exhibiting slightly better performance. Challenges such as signal noise and harmonics are addressed through iterative algorithms and signal reconstruction considerations. Future directions include identifying additional siren effects and conducting performance measurements on constrained hardware devices. Overall, this study presents viable EVD solutions suitable for real-time implementation and underscores the importance of adaptable and explainable AI methods in enhancing road safety.

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