Detection of alarm sounds in noisy environments

Carmel, Dean; Yeshurun, Ariel; Moshe, Yair

doi:10.23919/eusipco.2017.8081527

Cited by 27 publications

(6 citation statements)

References 11 publications

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“…[3] extracts the spectrum of the audio signal in their SVM-based siren detection model training. [4] uses the module difference function (MDF) then applies fully-connected layers for siren classification. [5] combines both time-domain features and frequency-domain features, and applies convolution neural networks to encode the signal history.…”

Section: B Related Workmentioning

confidence: 99%

Emergency Vehicles Audio Detection and Localization in Autonomous Driving

Sun,

Liu,

et al. 2021

Preprint

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Emergency vehicles in service have right-of-way over all other vehicles. Hence, all other vehicles are supposed to take proper actions to yield emergency vehicles with active sirens. As this task requires the cooperation between ears and eyes for human drivers, it also needs audio detection as a supplement to vision-based algorithms for fully autonomous driving vehicles. In urban driving scenarios, we need to know both the existence of emergency vehicles and their relative positions to us to decide the proper actions. We present a novel system from collecting the real-world siren data to the deployment of models using only two cost-efficient microphones. We are able to achieve promising performance for each task separately, especially within the crucial 10m to 50m distance range to react (the size of our ego vehicle is around 5m in length and 2m in width). The recall rate to determine the existence of sirens is 99.16% , the median and mean angle absolute error is 9.64°and 19.18°respectively, and the median and mean distance absolute error of 9.30m and 10.58m respectively within that range. We also benchmark various machine learning approaches that can determine the siren existence and sound source localization which includes direction and distance simultaneously within 50ms of latency.

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Section: B Related Workmentioning

confidence: 99%

Emergency Vehicles Audio Detection and Localization in Autonomous Driving

Sun,

Liu,

et al. 2021

Preprint

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“…Since current emergency vehicles produce siren sounds with different specifications, the configuration in [9] could not be flexible to use in general scenarios. An alarm sound detection system based on SVM in combination with feature selection of handcrafted features was proposed in [10]. It obtained an accuracy of more than 90% on evaluating the system's performance with a small dataset of 35 alarm sound samples and 35 background noise samples.…”

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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“…Y k (λ), S k (λ), and V k (λ) represent the noisy k th STFT coefficient of y(n), s(n) and v(n) respectively for frame λ. [29]. However, some of the aforementioned features are not efficient in terms of computational and space complexity.…”

Section: A Formulation and Input Featuresmentioning

confidence: 99%

“…In [26]- [28] we can see works on detecting the sirens of emergency vehicles like ambulance and police cars. In [29], an alarm sound de-tector based on support vector machine was proposed that is tested using several audio features. A simple siren detection system that runs in real-time is described in [30].…”

Section: Introductionmentioning

confidence: 99%

Design and Integration of Alert Signal Detector and Separator for Hearing Aid Applications

2020

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Alert signals like sirens and home alarms are important as they warn people of precarious situations. This work presents the detection and separation of these acoustically important alert signals, not to be attenuated as noise, to assist the hearing impaired listeners. The proposed method is based on convolutional neural network (CNN) and convolutional-recurrent neural network (CRNN). The developed method consists of two blocks, the detector block, and the separator block. The entire setup is integrated with speech enhancement (SE) algorithms, and before the compression stage, used in a hearing aid device (HAD) signal processing pipeline. The detector recognizes the presence of alert signal in various noisy environments. The separator block separates the alert signal from the mixture of noisy signals before passing it through SE to ensure minimal or no attenuation of the alert signal. It is implemented on a smartphone as an application that seamlessly works with HADs in real-time. This smartphone assistive setup allows the hearing aid users to know the presence of the alert sounds even when these are out of sight. The algorithm is computationally efficient with a low processing delay. The key contribution of this paper includes the development and integration of alert signal separator block with SE and the realization of the entire setup on a smartphone in real-time. The proposed method is compared with several state-of-the-art techniques through objective measures in various noisy conditions. The experimental analysis demonstrates the effectiveness and practical usefulness of the developed setup in real-world noisy scenarios.

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Detection of alarm sounds in noisy environments

Cited by 27 publications

References 11 publications

Emergency Vehicles Audio Detection and Localization in Autonomous Driving

Emergency Vehicles Audio Detection and Localization in Autonomous Driving

Acoustic-Based Emergency Vehicle Detection Using Convolutional Neural Networks

Design and Integration of Alert Signal Detector and Separator for Hearing Aid Applications

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