Low computational method for siren detection

Dobre, Robert Alexandru; Nita, Valentin A.; Ciobanu, Amelia; Negrescu, Cristian; Stanomir, Dumitru

doi:10.1109/siitme.2015.7342342

Cited by 23 publications

(12 citation statements)

References 2 publications

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“…Radio system requires interface of electronic noise, but our proposed system does not need any. In previous research, Low computational microcontroller-based siren sound detection system, done by the authors [15], a siren sound detection system with low processing power was designed. This proposed method outperforms the existing siren detection methods in terms of processing power and cost.…”

Section: Discussionmentioning

confidence: 99%

“…It takes care of the traffic problems, including the hardware installed in the vehicle, which confront the disadvantages of the linear traffic signal. While the severe weather conditions have no effect on Radio signals, additional hardware are required, which adds to the cost [14], and [15]. Based on the previous study, the search gap is the lack of identification of the proper communication system between software, hardware, emergency vehicle and devices in traffic light, and the server.…”

Section: Radio-controlled Systemsmentioning

confidence: 99%

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Lean Traffic Control (LTC) for Emergency Vehicles Applied in Developing Countries: Tehran Transport Planning

2018

Int. j. technol. eng. stud.

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Quick arrival of emergency assistance to the troubled area is immensely essential for ambulances, and other vehicles like police, fire engines, etc. For the distance between the departure and the final troubled points, the consumed traveling time is the most critical issue to be considered. Utmost attention should be taken into consideration at traffic lights and other congested intersections. This paper is advantageous and ideal in proposing, innovating, and developing a model, namely server-centric, which is a great asset in reducing the cost and producing better performance by applying an Internet of Things (IOT), by considering a central controller web server, and a microcontroller system by applying Global Positioning System (GPS) and Fuzzy Controller System (FCS) approach. This Lean Traffic Control (LTC) can dynamically and automatically control traffic signals in order to open the path to get the destination as soon as possible. It can be performed by considering three main steps to design: 1-Central controller web server, 2-Communications software application, and 3-Communications hardware application. The primary prototype of this project is ready and the future work will need the LTC system to be tested in real-world performance.

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Section: Discussionmentioning

confidence: 99%

Section: Radio-controlled Systemsmentioning

confidence: 99%

Lean Traffic Control (LTC) for Emergency Vehicles Applied in Developing Countries: Tehran Transport Planning

2018

Int. j. technol. eng. stud.

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“…There are several works based on microcontrollers [11]- [13] and hardware design [14], [15] for siren sound detection. In [11], the ambulance's siren sound could be detected by employing two times Fast Fourier Transform (FFT) on a dsPIC microcontroller.…”

Section: Related Workmentioning

confidence: 99%

“…Although we can quickly implement this algorithm on Texas Instrument TMS DSPs, it was concluded to be not foolproof and may result in false detection. R. Dobre et al [14], [15] proposed low-computational methods for siren detection based on analog electronics circuits. The authors used SPICE to simulate the initial design [14] and its improved version [15] of the circuit block used for siren detection.…”

Section: Related Workmentioning

confidence: 99%

Acoustic-Based Emergency Vehicle Detection Using Convolutional Neural Networks

Tran

Tsai

2020

IEEE Access

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This work investigates how to detect emergency vehicles such as ambulances, fire engines, and police cars based on their siren sounds. Recognizing that car drivers may sometimes be unaware of the siren warnings from the emergency vehicles, especially when in-vehicle audio systems are used, we propose to develop an automatic detection system that determines whether there are siren sounds from emergency vehicles nearby to alert other vehicles' drivers to pay attention. A convolutional neural network (CNN)-based ensemble model (SirenNet) with two network streams is designed to classify sounds of traffic soundscape to siren sounds, vehicle horns, and noise, in which the first stream (WaveNet) directly processes raw waveform, and the second one (MLNet) works with a combined feature formed by MFCC (Mel-frequency cepstral coefficients) and log-mel spectrogram. Our experiments conducted on a diverse dataset show that the raw data can complement the MFCC and log-mel features to achieve a promising accuracy of 98.24% in the siren sound detection. In addition, the proposed system can work very well with variable input length. Even for short samples of 0.25 seconds, the system still achieves a high accuracy of 96.89%. The proposed system could be helpful for not only drivers but also autopilot systems. INDEX TERMS Audio recognition, convolutional neural networks, emergency vehicle detection, siren sounds.

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“…In these cases, continuous recording is not possible on a long term, due to the important power consumption of the high frequency 2 channels recording system : 500mW for signal capture and storage. We have to cope with an issue described in [5], [6], [7] and [8]. Our recording system is working on a standard 12V − 1.2Ah lead battery having a global capacity of 14.4W h. In continuous recording, batteries would last only 30 hours, without including the energy necessary for processing signals and generating alerts.…”

Section: Introductionmentioning

confidence: 99%

Low-Power Wake-Up System based on Frequency Analysis for Environmental Internet of Things

Fourniol

Giès

Barchasz

et al. 2018

2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)

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Being used in for environmental and military Internet of Things (IoT), a low power wake-up system based on frequency analysis is presented in this paper. It aims at detecting continuously the presence of specific very high frequencies in the input acoustic signal of an embedded system. This can be used for detecting specific animal species, and for triggering a recording system or generating alerts. Used for harmful species detection, this helps to save harvests or to protect strict nature reserves. It can also be used for detecting the presence of drones in a specific restricted area. This acoustic low power wake-up system uses a simple 16 bits micro-controller (MCU), with a strong emphasis on the low power management of the system, having a target of continuous detection for at least one year on a single standard 1.2Ah-12V lead battery. For that, it makes the most of mixed analog and digital low power MCU modules. They are including comparators, timers and a special one present on Microchip MCU, called Charge Time Measurement Unit (CTMU). This is a driven constant current source for making time to frequency conversions at a very low power and algorithmic cost. Optimizing low power modes, this low power wake-up system based on frequency analysis has a power consumption of 0.56mW , leading to approximately 3 years of battery life on a single standard 1.2Ah-12V lead cell.

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Low computational method for siren detection

Cited by 23 publications

References 2 publications

Lean Traffic Control (LTC) for Emergency Vehicles Applied in Developing Countries: Tehran Transport Planning

Lean Traffic Control (LTC) for Emergency Vehicles Applied in Developing Countries: Tehran Transport Planning

Acoustic-Based Emergency Vehicle Detection Using Convolutional Neural Networks

Low-Power Wake-Up System based on Frequency Analysis for Environmental Internet of Things

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