Hadoop Deep Neural Network for offending drivers

Asadianfam, Shiva; Shamsi, Mahboubeh; Kenari, Abdolreza Rasouli

doi:10.1007/s12652-021-02924-4

Cited by 10 publications

(5 citation statements)

References 33 publications

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“…The most recent statistics show that globally, more than 30% of people between the ages of 18 and 50 own smartphones. Every smartphone contains a microphone, a channel for exchanging information, a location sensor, and the ability to install other software [29][30][31][32].…”

Section: Related Workmentioning

confidence: 99%

Fast Detection and Classification of Dangerous Urban Sounds Using Deep Learning

Momynkulov¹,

Dosbayev²,

Suliman³

et al. 2023

Computers, Materials &Amp; Continua

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Video analytics is an integral part of surveillance cameras. Compared to video analytics, audio analytics offers several benefits, including less expensive equipment and upkeep expenses. Additionally, the volume of the audio datastream is substantially lower than the video camera datastream, especially concerning real-time operating systems, which makes it less demanding of the data channel's bandwidth needs. For instance, automatic live video streaming from the site of an explosion and gunshot to the police console using audio analytics technologies would be exceedingly helpful for urban surveillance. Technologies for audio analytics may also be used to analyze video recordings and identify occurrences. This research proposed a deep learning model based on the combination of convolutional neural network (CNN) and recurrent neural network (RNN) known as the CNN-RNN approach. The proposed model focused on automatically identifying pulse sounds that indicate critical situations in audio sources. The algorithm's accuracy ranged from 95% to 81% when classifying noises from incidents, including gunshots, explosions, shattered glass, sirens, cries, and dog barking. The proposed approach can be applied to provide security for citizens in open and closed locations, like stadiums, underground areas, shopping malls, and other places.

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Section: Related Workmentioning

confidence: 99%

Fast Detection and Classification of Dangerous Urban Sounds Using Deep Learning

Momynkulov¹,

Dosbayev²,

Suliman³

et al. 2023

Computers, Materials &Amp; Continua

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“…An assortment of expansive datasets was engaged to scrutinize the sounds termed as "hazardous." The Environmental Sound Classification (ESC-50) dataset [29] emerged as the preferred choice for program evaluation. From its vast repertoire of 2,000 auditory samples, a curated subset of approximately 300 sounds was employed.…”

Section: A Datamentioning

confidence: 99%

Convolutional LSTM Network for Real-Time Impulsive Sound Detection and Classification in Urban Environments

Altayeva,

Omarov,

Tileubay

et al. 2023

IJACSA

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In recent years, the escalating challenges of noise pollution in urban environments have necessitated the development of more sophisticated sound detection and classification systems. This research introduces a novel approach employing a Convolutional Long Short-Term Memory (ConvLSTM) network tailored for real-time impulsive sound detection in metropolitan landscapes. Impulsive sounds, characterized by sudden onsets and short durations-such as honking, abrupt shouts, or breaking glass-are inherently sporadic but can significantly impact urban soundscapes and the well-being of city dwellers. Traditional sound detection mechanisms often falter in identifying these ephemeral noises amidst the cacophony of urban life. The ConvLSTM network proposed in this study amalgamates the spatial feature learning capabilities of Convolutional Neural Networks (CNN) with the temporal sequence retention attributes of LSTM, culminating in an architecture that excels in both sound detection and classification tasks. The model was trained and evaluated on a comprehensive dataset sourced from various urban settings and demonstrated commendable proficiency in discerning impulsive sounds with minimal false positives. Furthermore, the system's real-time processing capabilities ensure timely interventions, paving the way for smarter noise management in cities. This research not only propels the frontier of impulsive sound detection but also underscores the potential of ConvLSTM in addressing multifaceted urban challenges.

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“…3. And in order to see the difference for ourselves, we can take a different sound and compare their graphs, it is enough to display them through the Matplotlib library, which will allow us to do this [29]. To do this, we import and take another example with a mono channel for comparison, this will be a barking dog.…”

Section: Sound Analysis and Visualizationmentioning

confidence: 99%

Development of a Framework for Classification of Impulsive Urban Sounds using BiLSTM Network

Katayev,

Altayeva,

Abduraimova

et al. 2023

IJACSA

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Urban environments are awash with myriad sounds, among which impulsive noises stand distinct due to their brief and often disruptive nature. As cities evolve and expand, the accurate classification and management of these impulsive sounds become paramount for urban planners, environmental scientists, and public health advocates. This paper introduces a novel framework leveraging the Bidirectional Long Short-Term Memory (BiLSTM) Network for the systematic categorization of impulsive urban sounds. Traditional methodologies often falter in recognizing the nuanced intricacies of such noises. In contrast, the presented BiLSTM-based approach adapts to the temporal variability intrinsic to these sounds, thereby enhancing classification accuracy. The research harnesses an expansive dataset, curated from various urban settings, to train and validate the model. Preliminary findings suggest that our BiLSTM framework outperforms existing models, with a marked increase in both specificity and sensitivity metrics. The outcome of this study holds profound implications for city acoustics management, noise pollution control, and urban health interventions. Moreover, the framework's adaptability paves the way for its application across diverse acoustic landscapes beyond the urban realm. Future endeavors should seek to further optimize the model by integrating more diverse soundscapes and addressing potential biases in data collection.

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Hadoop Deep Neural Network for offending drivers

Cited by 10 publications

References 33 publications

Fast Detection and Classification of Dangerous Urban Sounds Using Deep Learning

Fast Detection and Classification of Dangerous Urban Sounds Using Deep Learning

Convolutional LSTM Network for Real-Time Impulsive Sound Detection and Classification in Urban Environments

Development of a Framework for Classification of Impulsive Urban Sounds using BiLSTM Network

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