Automated detection of alarm sounds

Lutfi, Robert A.; Heo, Inseok

doi:10.1121/1.4734555

Cited by 10 publications

(8 citation statements)

References 2 publications

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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]. However, these methods' accuracy has generally been limited, and their performance under noisy conditions is poor.…”

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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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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“…The automatic alarm sound detection was previously investigated for the purposes of hearing impaired assistance and hearing support in noisy conditions [12] – [14] . To our knowledge, research on the topic was first reported in [15] , where the detection of various real-world alarm sounds was addressed.…”

Section: Introductionmentioning

confidence: 99%

“…While the model-based approach is also followed in [12] , most of the posterior works adhere to the non-model-based approach. For instance, a simple signal processing based method was reported in [13] , where an autocorrelation function, used to exploit the long-term periodicity of alarms, is compared to a threshold. In [14] amplitude periodicity in a specific frequency bandwidth is detected using a decision tree based on zero-crossing rate of the autocorrelation of the signal envelope.…”

Section: Introductionmentioning

confidence: 99%

A Knowledge-Based Approach to Automatic Detection of Equipment Alarm Sounds in a Neonatal Intensive Care Unit Environment

Raboshchuk

Nadeu

Jančovič

et al. 2018

IEEE J. Transl. Eng. Health Med.

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A large number of alarm sounds triggered by biomedical equipment occur frequently in the noisy environment of a neonatal intensive care unit (NICU) and play a key role in providing healthcare. In this paper, our work on the development of an automatic system for detection of acoustic alarms in that difficult environment is presented. Such automatic detection system is needed for the investigation of how a preterm infant reacts to auditory stimuli of the NICU environment and for an improved real-time patient monitoring. The approach presented in this paper consists of using the available knowledge about each alarm class in the design of the detection system. The information about the frequency structure is used in the feature extraction stage, and the time structure knowledge is incorporated at the post-processing stage. Several alternative methods are compared for feature extraction, modeling, and post-processing. The detection performance is evaluated with real data recorded in the NICU of the hospital, and by using both frame-level and period-level metrics. The experimental results show that the inclusion of both spectral and temporal information allows to improve the baseline detection performance by more than 60%.

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“…All tests were done at 0 dB signal-to-noise ratio (SNR), and both approaches were stated to perform poorly. A much simpler approach for automated detection of alarm sounds is described in [11]. Four classes of alarm sounds (bells/chimes, buzzers/beepers, horns/whistles, and sirens) are detected in four noise backgrounds (cafeteria, park, traffic, and music) based on the normalized autocorrelation function and the overall sound level.…”

Section: Introductionmentioning

confidence: 99%

Detection of alarm sounds in noisy environments

Carmel

Yeshurun

Moshe

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Sirens and alarms play an important role in everyday life since they warn people of hazardous situations, even when these are out of sight. Automatic detection of this class of sounds can help hearing impaired or distracted people, e.g., on the road, and contribute to their independence and safety. In this paper, we present a technique for the detection of alarm sounds in noisy environments. The technique is not limited to particular alarms and can detect most electronically generated alerting sounds within 200 ms. We consider a set of acoustic features and use the ReliefF algorithm to select only the ones that best differentiate between alarms and other sounds. We use an SVM classifier as the detector. On the tested dataset, consisting of several dozen alarm sounds and several dozen background noises, the proposed technique shows an accuracy of 98% per audio frame. With a larger training dataset, this result is expected to substantially improve.

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Automated detection of alarm sounds

Cited by 10 publications

References 2 publications

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

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

A Knowledge-Based Approach to Automatic Detection of Equipment Alarm Sounds in a Neonatal Intensive Care Unit Environment

Detection of alarm sounds in noisy environments

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