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A practical method for measuring waveform similarity of real recorded gunshot data in the presence of noise and interference is presented, including a frequency domain sample cross correlation method and a short-time cross correlation technique that corresponds to the same time base as a short-time Fourier transform spectrogram. Forensic audio recordings often contain impulsive events like gunshots, and their examination may require comparative analysis using a measure of similarity between multiple events or a comparison with a separate but known recorded event. The sample cross correlation is a widely used and accepted statistical analysis procedure that has become a standard technique for measuring the similarity between two impulsive and finite duration signals like recorded gunshot sounds. Problems interpreting correlation results can arise in real recorded data where the signals are noisy and cluttered with interfering signals. Standard signal processing techniques like filtering and spectral subtraction can distort short duration and wideband signal waveforms like gunshots, and can significantly affect their cross correlation results.
Author (s): Beck, Steven D.;
Affiliation:
Beck Audio Forensics, Austin TX, USA
(See document for exact affiliation information.)
Publication Date:
2019-06-06
DOI:
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Beck, Steven D.; 2019; A Short-Time Cross Correlation with Application to Forensic Gunshot Analysis [PDF]; Beck Audio Forensics, Austin TX, USA; Paper 11; Available from: https://aes.org/publications/elibrary-page/?id=20464
Beck, Steven D.; A Short-Time Cross Correlation with Application to Forensic Gunshot Analysis [PDF]; Beck Audio Forensics, Austin TX, USA; Paper 11; 2019 Available: https://aes.org/publications/elibrary-page/?id=20464
@inproceedings{Beck2019a,
title={{A Short-Time Cross Correlation with Application to Forensic Gunshot Analysis}},
author={Beck, Steven D.},
year={2019},
month={jun},
booktitle={Journal of the Audio Engineering Society},
publisher={Paper 11; AES Conference: 2019 AES International Conference on Audio Forensics; June 2019},
number={11},
organization={AES},
}
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