Augmented warning sound detection for hearing protectors

Bernstein, Eric R.; Brammer, Anthony J.; Yu, Gongqiang

doi:10.1121/1.4835135

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…In literature, there are some works to detect the alert signals and thereby enhance environmental awareness. In [ 24 ], a simulated environment is generated and a set of pre-selected alarm sounds are detected through cross-correlation techniques. Artificial neural network (ANN) based pattern matching technique was used to detect police vehicle sirens in [ 25 ].…”

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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Section: Introductionmentioning

confidence: 99%

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

2020

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“…Previous studies of automatic audible alarm recognition have been primarily in the settings of industrial and traffic alarms [7][8][9][10][11]. Approaches described in the literature have included sinusoidal modeling, machine learning, longest common sequence identification, and amplitude-based periodicity detection [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of an Algorithm for the Identification of Audible Medical Alarms

Potnuru

Epstein

McNeer

et al. 2020

Cureus

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Audible medical alarms are ubiquitous in acute healthcare environments, but caregivers cannot reliably identify them. Furthermore, background noise and psychoacoustic factors can interfere with alarm recognition and contribute to alarm fatigue. We developed and validated an acoustic digital signal processing algorithm for the automatic identification of audible medical alarms. The algorithm uses the short-time Fourier transform to decompose audio signals and extract the alarm sounds' fundamental frequencies, harmonics, and periodicity. This information is then used to classify and recognize these sounds. The identification algorithm demonstrates robust performance (F1 score of 93% to 100%) and 100% negative predictive value in identifying single or multiple medical audible alarms under both quiet and noisy conditions. The algorithm we developed represents a robust approach for the identification of audible medical alarms that perform with high accuracy in noisy environments. It can be used to identify and classify alarms in medical settings for research and clinical purposes.

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“…The problem with the perception of back-up alarm signals was deemed serious enough that a dedicated electronic system for earmuffs was designed and introduced. The system functions to support the detection of such signals during the use of earmuffs in noisy environments [14]. The ability to perceive back-up alarm signals when using hearing protectors was also assessed [15].…”

Section: Introductionmentioning

confidence: 99%

“…We also aimed to examine the impact of changes in the amplification of the electronic system of level-dependent hearing protectors. The tests were carried out in the presence of ambient noise recorded under industrial conditions containing impulse components, which is in contrast to the previous study [11,13,14,15,17].…”

Section: Introductionmentioning

confidence: 99%

Localization of Vehicle Back-Up Alarms by Users of Level-Dependent Hearing Protectors under Industrial Noise Conditions Generated at a Forge

Młyński

Kozłowski

2019

IJERPH

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The use of hearing protectors in various noisy workplaces is often necessary. For safety reasons, auditory information may be required to correctly localize the direction of an auditory danger signal. The purpose of this study was to verify if the selection of a specific level-dependent hearing protector may be important for the ability to localize a vehicle back-up alarm signal. The laboratory conditions reflected industrial conditions, under which an impulse noise was emitted against a background of continuous noise. A passive mode and a level-dependent mode (maximum and incomplete amplification) were considered. Four different models of level-dependent earmuffs and one model of level-dependent earplugs were included in the tests. The tests enabled differentiation between the individual hearing protectors. The use of earplugs in level-dependent mode did not significantly affect the ability to correctly localize the back-up alarm signal. For the earmuffs, the global assessment of the impact of a mode change revealed that, depending on the model of the earmuffs, the impact may be insignificant, but may also result in considerable impairment of the ability to localize the back-up alarm signal.

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Augmented warning sound detection for hearing protectors

Cited by 5 publications

References 6 publications

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

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

Development and Validation of an Algorithm for the Identification of Audible Medical Alarms

Localization of Vehicle Back-Up Alarms by Users of Level-Dependent Hearing Protectors under Industrial Noise Conditions Generated at a Forge

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