Measurement and Characterization of Hearing Protection Devices in the Presence of Impulse Sound

Tardif, Bruno; Lo, David; Goubran, Rafik

doi:10.1109/memea52024.2021.9478767

Cited by 1 publication

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References 18 publications

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“…The second contribution is a method for improving the identification of gunshots with modern image classification, combined with machine-learning processes, to accurately identify the type of gun based on the gunshot impulse sound. This contribution is presented in Chapter 4 and has been published in the following paper [2]:…”

Section: Contributionsmentioning

confidence: 99%

Gunshots Sound Analysis, Identification, and Impact on Hearing

Tardif¹

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Analyzing the sound resulting from a gunshot can help identify the type of gun used. This can help determine the appropriate public safety actions when a gunshot sound is detected in a public space. Furthermore, analyzing the sound can lead to a better understanding of the impact of this sound on hearing. For example, exposure to gunshot sounds can cause hearing impairments. Measuring these sounds from various locations and analyzing them can improve the design of hearing protectors and help enact regulations to minimize the impact of gunshot sounds on hearing impairments.In this thesis, acoustic data was collected from different guns and a mortar for analysis. To capture their sound including any non-symmetric sound propagation from the gunshots, 27 high dynamic range pressure 1/8" microphones were placed around the weapons forming a polar grid pattern. Audio signals from all microphones were captured at 204.8 kHz sampling rate synchronously to preserve the fidelity of the impulse nature of the gunshots. This thesis entails two types of analysis. The first is an image-based analysis method to take advantage of the recent advancement of image recognition techniques. In this thesis, we used Continuous Wavelet Transform (CWT) and Short-Time Fourier Transform (STFT) to obtain the time-frequency spectrogram of the gunshot recordings as images. We then employed a two-stage machine-learning classifier to process the images to identify the specific gun or mortar the gunshot was originated from. Classification accuracy of 98.1% was achieved in this example. A second type of analysis was conducted using a novel method for characterizing acoustic attenuation properties that respects the iii spectral sensitivity of the human ear and preserves the energy in its octave bands at the same time.

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Section: Contributionsmentioning

confidence: 99%

Gunshots Sound Analysis, Identification, and Impact on Hearing

Tardif¹

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Measurement and Characterization of Hearing Protection Devices in the Presence of Impulse Sound

Cited by 1 publication

References 18 publications

Gunshots Sound Analysis, Identification, and Impact on Hearing

Gunshots Sound Analysis, Identification, and Impact on Hearing

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