Journals / Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji / 2018 / Cilt: 6 - Sayı: 2

The Impact of Acoustic Parameters on the Classification of Firearms Using Gunshots

Silah Sesleri Kullanılarak Ateşli Silahların Sınıflandırılmasında Akustik Parametrelerin Etkisi

Pages
462–469
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The “Carl Gustav M45-b” and “Tokarev PPSh” was identified as the weapon with the highest recognition rate and the lowest “Tikka Model T3” recognition rate.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Actions related to firearms are an increasing source of concern for both security forces and the public.
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Key Findings & Data:In the study, 511 gunshots recordings of 23 firearms were used.
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Conclusion & Impact:The “Carl Gustav M45-b” and “Tokarev PPSh” was identified as the weapon with the highest recognition rate and the lowest “Tikka Model T3” recognition rate.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Silah sesi tanıma · Silah türü sınıflandırma · Akustik analiz

Abstract

Actions related to firearms are an increasing source of concern for both security forces and the public. There are various commercial or experimental studies for the classification of gunshots. The purpose of this study is to examine the effects of acoustic analysis on the detection of firearms by using gunshots. In the study, 511 gunshots recordings of 23 firearms were used. The formant frequencies, MFCC, LPCC and energy parameters for acoustic analysis are examined. The effectiveness of the acoustic parameters in classifying the weapons was analyzed statistically and all the parameters used were found to be effective. The classification performance was tested with the MLP classifier and the firearm recognition rate was 71.56%. The “Carl Gustav M45-b” and “Tokarev PPSh” was identified as the weapon with the highest recognition rate and the lowest “Tikka Model T3” recognition rate.

Özet

Ateşli silahlarla ilgili eylemler hem güvenlik güçleri hem de halk için artan bir endişe kaynağıdır. Silah seslerinin sınıflandırılması için ticari veya deneysel çeşitli çalışmalar mevcuttur. Bu çalışmanın amacı silah seslerini kullanarak ateşli silahların türünü tespit etmede akustik analizin etkilerinin incelenmesidir. Çalışmada 23 ateşli silaha ait 510 atış ses kaydı kullanılmıştır. Akustik analiz için formant frekansları, MFCC, LPCC ve enerji parametreleri incelenmiştir. Akustik parametrelerin silahları sınıflandırmadaki etkinliği istatistiksel olarak analiz edilmiş ve kullanılan tüm parametrelerin etkili olduğu görülmüştür. MLP sınıflandırıcı ile sınıflandırma performansı test edilmiş ve ateşli silah tanıma oranı %71.56 elde edilmiştir. Tanıma oranı en yüksek silah türü “Carl Gustav M45” ve “Tokarev PPSh”, tanıma oranı en düşük olan silah “Tikka Model T2” olarak tespit edilmiştir.

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Cite This Article

DÜĞENCİ, M., ÖZSEVEN, T. (2022). The Impact of Acoustic Parameters on the Classification of Firearms Using Gunshots. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, Cilt: 6 - Sayı: 2, 462–469. https://ajindex.com/articles/522135

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References (13)

  1. Chacon-Rodriguez, A., Julian, P., Castro, L., Alvarado, P. and Hernández, N., “Evaluation of Gunshot Detection Algorithms”, IEEE Transactions on Circuits and Systems I: Regular Papers, Cilt 58, No 2, 363-373, 2011.
  2. Ahmed, T., Uppal, M. and Muhammad, A., “Improving Efficiency and Reliability of Gunshot Detection Systems”, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 513-517, May 2013.
  3. Maher, R. C., “Modeling and Signal Processing of Acoustic Gunshot Recordings”, In Digital Signal Processing Workshop, 12th-Signal Processing Education Workshop, 257-261, September 2006. Fernández, A. I. T., “Propagación del Sonido En Bosques: Análisis Comparativo De Las Medidas in Situ”, En Laboratorio y de Los Valores Predichos Por un Modelo, 2002.
  4. Arslan, Y. and Güldoğan, B., “Impulsive Sound Detection and Gunshot Recognition”, In Signal Processing and Communications Applications Conference, 511-514, May 2015.
  5. Maher, R. C., “Acoustical Characterization of Gunshots”, In Signal Processing Applications for Public Security and Forensics, 1-5, April 2007.
  6. Kılıç, M.A, ve Okur E., “CSL ve Dr.Speech ile Ölçülen Temel Frekans ve Pertürbasyon Değerlerinin Karşılaştırılması”, K.B.B. İhtisas Dergisi, No 8, 152-157, 2001.
  7. Özbal, E.A.,”Septum Devisyonlu Hastaların Septoplast Operasyonu Öncesi ve Sonrası Akustik Ses Analizi ile Değerlendirilmesi”, Uzmanlık Tezi, Şişli Etfal Hastanesi, İstanbul, 2008.
  8. Sataloff, R. T., “Treatment of Voice Disorders”, Plural Publishing, 2005. İnternet: http://www.raytheon.com/, 20.02.2017.
  9. Hrabina, M. and Sigmund, M., “Acoustical Detection of Gunshots”, 25th International Conference In Radioelektronika, 150-153, April 2015. İnternet: http://www.shotspotter.com, 20.02.2017. İnternet: http://www.cobham.com, 20.02.2017. İnternet: https://www.qinetiq-na.com, 20.02.2017.
  10. Gerosa, L., Valenzise, G., Tagliasacchi, M., Antonacci, F. and Sarti, A., “Scream and Gunshot Detection in Noisy Environments”, In Signal Processing Conference, 1216-1220, September 2007.
  11. Freire, I. L. and Apolinário Jr, J. A., “Gunshot Detection in Noisy Environments”, In Proceeding of the 7th International Telecommunications Symposium, Manaus, Brazil, 1-4, September 2010.
  12. Djeddou, M. and Touhami, T., “Classification and Modeling of Acoustic Gunshot Signatures”, Arabian Journal for Science & Engineering, Cilt 38, No 12, 2013.
  13. Suman, P., Karan, S., Singh, V. and Maringanti, R., “Algorithm for Gunshot Detection Using Mel-frequency Cepstrum Coefficients (MF