Deep learning based urban sound classification and ambulance siren detector using spectrogram

Pramanick, Dipro; Ansar, Haaris; Kumar, Hemant; Sk, Pranav; Tengshe, Richa; Fatimah, Binish

doi:10.1109/icccnt51525.2021.9579778

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…For example, Scarpiniti et al [62] implemented a method in construction sites to detect hazards and unmanned activity monitoring. Also, robotic scene recognition (Aziz et al [63]), drones (Ibrahim et al [64]), siren detection, particularly to allow the priority vehicles to arrive at its destination sooner (Pramanick et al [65], Fatimah et al [66]), are some applications that can take advantage of urban sound classification systems.…”

Section: Applicationsmentioning

confidence: 99%

Sound Classification and Processing of Urban Environments: A Systematic Literature Review

Nogueira

Oliveira

Machado

et al. 2022

Sensors

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Audio recognition can be used in smart cities for security, surveillance, manufacturing, autonomous vehicles, and noise mitigation, just to name a few. However, urban sounds are everyday audio events that occur daily, presenting unstructured characteristics containing different genres of noise and sounds unrelated to the sound event under study, making it a challenging problem. Therefore, the main objective of this literature review is to summarize the most recent works on this subject to understand the current approaches and identify their limitations. Based on the reviewed articles, it can be realized that dl architectures, attention mechanisms, data augmentation techniques, and pretraining are the most crucial factors to consider while creating an efficient sound classification model. The best-found results were obtained by Mushtaq and Su, in 2020, using a DenseNet-161 with pretrained weights from ImageNet, and NA-1 and NA-2 as augmentation techniques, which were of 97.98%, 98.52%, and 99.22% for UrbanSound8K, ESC-50, and ESC-10 datasets, respectively. Nonetheless, the use of these models in real-world scenarios has not been properly addressed, so their effectiveness is still questionable in such situations.

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Section: Applicationsmentioning

confidence: 99%

Sound Classification and Processing of Urban Environments: A Systematic Literature Review

Nogueira

Oliveira

Machado

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…This approach neglects to account for the variety of siren types and specifications, limiting the system's ability to generalize for real-world applications. Secondly, previous works [14][15][16][17][18] often rely on training the shallow learning algorithms involved in the use of handcrafted features or microcontrollers employed for signal processing tasks. As a result, these systems exhibit inferior detection accuracies, typically falling below 90%, and suffer from computational inefficiency.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Emergency Vehicle Detection: A Deep Learning Approach with Multimodal Fusion

Zohaib,

Asim,

ELAffendi

2024

Mathematics

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Emergency vehicle detection plays a critical role in ensuring timely responses and reducing accidents in modern urban environments. However, traditional methods that rely solely on visual cues face challenges, particularly in adverse conditions. The objective of this research is to enhance emergency vehicle detection by leveraging the synergies between acoustic and visual information. By incorporating advanced deep learning techniques for both acoustic and visual data, our aim is to significantly improve the accuracy and response times. To achieve this goal, we developed an attention-based temporal spectrum network (ATSN) with an attention mechanism specifically designed for ambulance siren sound detection. In parallel, we enhanced visual detection tasks by implementing a Multi-Level Spatial Fusion YOLO (MLSF-YOLO) architecture. To combine the acoustic and visual information effectively, we employed a stacking ensemble learning technique, creating a robust framework for emergency vehicle detection. This approach capitalizes on the strengths of both modalities, allowing for a comprehensive analysis that surpasses existing methods. Through our research, we achieved remarkable results, including a misdetection rate of only 3.81% and an accuracy of 96.19% when applied to visual data containing emergency vehicles. These findings represent significant progress in real-world applications, demonstrating the effectiveness of our approach in improving emergency vehicle detection systems.

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Audio Classification of Emergency Vehicle Sirens Using Recurrent Neural Network Architectures

Shah,

Singh,

Singh

2023

Algorithms for Intelligent Systems

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Deep learning based urban sound classification and ambulance siren detector using spectrogram

Cited by 5 publications

References 27 publications

Sound Classification and Processing of Urban Environments: A Systematic Literature Review

Sound Classification and Processing of Urban Environments: A Systematic Literature Review

Enhancing Emergency Vehicle Detection: A Deep Learning Approach with Multimodal Fusion

Audio Classification of Emergency Vehicle Sirens Using Recurrent Neural Network Architectures

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