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Deep Spectrogram Learning for Gunshot Classification: A Comparative Study of CNN Architectures and Time-Frequency Representations

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Published in:Journal of Imaging vol. 11, no. 8 (2025), p. 281-307
Main Author: Pafan, Doungpaisan
Other Authors: Peerapol, Khunarsa
Published:
MDPI AG
Subjects:
United States > US
Accuracy
Datasets
Deep learning
Wavelet transforms
Law enforcement
Color imagery
Racial profiling
Artificial neural networks
Audio recordings
Public safety
Small arms
Computer vision
Automation
Machine learning
Image processing
Representations
Pattern recognition
Sound
Comparative studies
Artificial intelligence
Fourier transforms
Spectrograms
Time-frequency analysis
Neural networks
Classification
Image classification
Surveillance
Gun violence
Acoustics
Murders & murder attempts
Surveillance systems
Online Access:Citation/Abstract
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Abstract:Gunshot sound classification plays a crucial role in public safety, forensic investigations, and intelligent surveillance systems. This study evaluates the performance of deep learning models in classifying firearm sounds by analyzing twelve time–frequency spectrogram representations, including Mel, Bark, MFCC, CQT, Cochleagram, STFT, FFT, Reassigned, Chroma, Spectral Contrast, and Wavelet. The dataset consists of 2148 gunshot recordings from four firearm types, collected in a semi-controlled outdoor environment under multi-orientation conditions. To leverage advanced computer vision techniques, all spectrograms were converted into RGB images using perceptually informed colormaps. This enabled the application of image processing approaches and fine-tuning of pre-trained Convolutional Neural Networks (CNNs) originally developed for natural image classification. Six CNN architectures—ResNet18, ResNet50, ResNet101, GoogLeNet, Inception-v3, and InceptionResNetV2—were trained on these spectrogram images. Experimental results indicate that CQT, Cochleagram, and Mel spectrograms consistently achieved high classification accuracy, exceeding 94% when paired with deep CNNs such as ResNet101 and InceptionResNetV2. These findings demonstrate that transforming time–frequency features into RGB images not only facilitates the use of image-based processing but also allows deep models to capture rich spectral–temporal patterns, providing a robust framework for accurate firearm sound classification.
ISSN:2313-433X
DOI:10.3390/jimaging11080281
Source:Advanced Technologies & Aerospace Database