An automatic siren detection algorithm using Fourier Decomposition Method and MFCC

Fatimah, Binish; Preethi, A; Hrushikesh,; B, Akhilesh Singh; Kotion, Harshanikethan R

doi:10.1109/icccnt49239.2020.9225414

Cited by 22 publications

(6 citation statements)

References 11 publications

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“…After creating the border boxes, classification was carried out in two stages. The study [19]suggested audio and vision-based methods for detection of emergency vehicles use Wave-ResNet for sound processing and YOLO for image processing. The two main issues with the work done so far in Emergency Vehicle Detection are feature selections that could be more task-specific, which reduces efficiency, and a lack of enthusiasm for building an effective model at the model level.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart City Traffic Management: Acoustic-Based Vehicle Detection Using Stacking-Based Ensemble Deep Learning Approach

Shabbir,

Cheema,

Ullah

et al. 2024

IEEE Access

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Acoustic data analysis has emerged as a critical area of exploration for the detection of different events for quick actions in smart traffic management systems, particularly in traffic management and safety for a step toward smart cities. A specific challenge is precisely classifying road noises and emergency vehicles using sound, which is essential for speeding up emergency response times and improving traffic flow management. While existing solutions address this problem, there is opportunity for enhancement in terms of precision and accuracy to enhance the traffic flow in a sustainable smart city through a demanding and innovative technique. In this study, we suggest stacking ensemble deep learning techniques to intelligently classify emergency vehicle sirens from various background noises using a data set of traffic collected on roads via microphone sensors. The ensemble model incorporates Multi-Layer Perceptron (MLP) and Deep Neural Network (DNN) as base-learners, with an LSTM model as a metalearner. This approach not only optimizes model efficiency but also facilitates advanced feature engineering to extract useful features including Mel Frequency Cepstral Coefficients (MFCC), Z-score, root mean square (RMS), spectral centroids, spectral flux, mel spectrogram, chroma, contrast, and Tonnetz. Using these features, our proposed Stacking Ensemble LSTM successfully classified traffic noises and emergency vehicle sirens with the highest efficiency. Upon evaluation of the test set of data for our proposed model, it has gained an accuracy of 99.12% with F1 scores ranging from 98%. This significant improvement highlights the dominance of our proposed model approach over prior research. Our proposed model presents assurance in advancing traffic control and safety statutes, demonstrating potential applicability in daily intelligent transportation systems.INDEX TERMS Data-driven urban planning; smart city technologies; intelligent transportation systems; smart urban infrastructure; sustainable urban development; Smart traffic management; urban mobility solutions; urban sensor networks;

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Section: Literature Reviewmentioning

confidence: 99%

Smart City Traffic Management: Acoustic-Based Vehicle Detection Using Stacking-Based Ensemble Deep Learning Approach

Shabbir,

Cheema,

Ullah

et al. 2024

IEEE Access

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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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“…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%

“…As a result, these systems exhibit inferior detection accuracies, typically falling below 90%, and suffer from computational inefficiency. For instance, the system described in [16] requires 8 s to complete a single detection. To address these limitations, this study proposes the development of an AEVD system that incorporates an attention mechanism following the groundwork provided in [19] to increase the accuracy and efficiency of our AEVD model.…”

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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An automatic siren detection algorithm using Fourier Decomposition Method and MFCC

Cited by 22 publications

References 11 publications

Smart City Traffic Management: Acoustic-Based Vehicle Detection Using Stacking-Based Ensemble Deep Learning Approach

Smart City Traffic Management: Acoustic-Based Vehicle Detection Using Stacking-Based Ensemble Deep Learning Approach

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

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

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