Study of a low frequency emergency siren in comparison to traditional siren technology

Angione, Frank; Novak, Colin; Imeson, Chris; Lehman, Ashley; Merwin, Ben; Pagliarella, Tom; Samardzic, Nikolina; D'Angela, Peter; Ule, Helen

doi:10.1121/2.0000601

Cited by 4 publications

(1 citation statement)

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“…A possible solution is to imitate the transparency mode on AirPods Pro devised by Apple [1]. In specific, the external microphone can measure the outside sounds and replay the meaningful parts (like sirens, between 725-1600 Hz [36]) through the onboard speakers. Besides, wearing in-ear earbuds for a long time might cause discomfort to the user.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

OESense

Ferlini

Mascolo

2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

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Smart earbuds are recognized as a new wearable platform for personal-scale human motion sensing. However, due to the interference from head movement or background noise, commonly-used modalities (e.g. accelerometer and microphone) fail to reliably detect both intense and light motions. To obviate this, we propose OESense, an acoustic-based in-ear system for general human motion sensing. The core idea behind OESense is the joint use of the occlusion effect (i.e., the enhancement of low-frequency components of bone-conducted sounds in an occluded ear canal) and inward-facing microphone, which naturally boosts the sensing signal and suppresses external interference. We prototype OESense as an earbud and evaluate its performance on three representative applications, i.e., step counting, activity recognition, and hand-to-face gesture interaction. With data collected from 31 subjects, we show that OESense achieves 99.3% step counting recall, 98.3% recognition recall for 5 activities, and 97.0% recall for five tapping gestures on human face, respectively. We also demonstrate that OESense is compatible with earbuds' fundamental functionalities (e.g. music playback and phone calls). In terms of energy, OESense consumes 746 mW during data recording and recognition and it has a response latency of 40.85 ms for gesture recognition. Our analysis indicates such overhead is acceptable and OESense is potential to be integrated into future earbuds. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing systems and tools.

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Section: Discussion and Limitationsmentioning

confidence: 99%

OESense

Ferlini

Mascolo

2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

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Review of Emergency Vehicle Detection Techniques by Acoustic Signals

Choudhury,

Nandi

2023

Trans Indian Natl. Acad. Eng.

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The spectrogram, method of reassignment, and frequency-domain beamforming

Averbuch

2021

The Journal of the Acoustical Society of America

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A smeared spectrogram is a result of the smoothing kernel in the short-time Fourier-transform (STFT). Besides the smeared energy, time and frequency phase information is also smeared, i.e., spectral components may contain imprecise phase information. The STFT is also used as the basis for more advanced signal processing techniques such as frequency-domain beamforming and cross correlation (CC). Both methods seek the delay time between signals by exploring phase-shifts in the frequency domain. Due to the inexact phase information in some of the time-frequency elements, their phase shifts are incorrect. This study re-introduces the reassigned spectrogram (RS) as a measure to fix the STFT artifacts. Moreover, it is shown that by using the RS, phase shifts can be optimized and improve beamforming and CC results. Synthetic and recorded data are used to show the advantage of using the RS in time-frequency analysis, CC, and beamforming. Results show that, subject to certain constraints, the RS provides exact time-frequency representation of deterministic signals and significantly improve CC and beamforming results. Array analysis of infrasonic signals shows that better results are obtained by either the RS- or STFT-based analysis depending on the signals' spectral components and noise levels.

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Study of a low frequency emergency siren in comparison to traditional siren technology

Cited by 4 publications

References 0 publications

OESense

OESense

Review of Emergency Vehicle Detection Techniques by Acoustic Signals

The spectrogram, method of reassignment, and frequency-domain beamforming

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