Silah Sesleri Kullanılarak Ateşli Silahların Sınıflandırılmasında Akustik Parametrelerin Etkisi
Year 2018,
, 462 - 469, 30.06.2018
Turgut Özseven
,
Muharrem Düğenci
Abstract
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.
References
- 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.
- 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.
- 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.
- Arslan, Y. and Güldoğan, B., “Impulsive Sound Detection and Gunshot Recognition”, In Signal Processing and Communications Applications Conference, 511-514, May 2015.
- Maher, R. C., “Acoustical Characterization of Gunshots”, In Signal Processing Applications for Public Security and Forensics, 1-5, April 2007.
- 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.
- Özbal, E.A.,”Septum Devisyonlu Hastaların Septoplast Operasyonu Öncesi ve Sonrası Akustik Ses Analizi ile Değerlendirilmesi”, Uzmanlık Tezi, Şişli Etfal Hastanesi, İstanbul, 2008.
- Sataloff, R. T., “Treatment of Voice Disorders”, Plural Publishing, 2005.
- İnternet: http://www.raytheon.com/, 20.02.2017.
- 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.
- 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.
- 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.
- Djeddou, M. and Touhami, T., “Classification and Modeling of Acoustic Gunshot Signatures”, Arabian Journal for Science & Engineering, Cilt 38, No 12, 2013.
- Suman, P., Karan, S., Singh, V. and Maringanti, R., “Algorithm for Gunshot Detection Using Mel-frequency Cepstrum Coefficients (MFCC)”, In Proceedings of Ninth International Conference on Wireless Communication and Sensor Networks, 155-166, 2014.
- Lojka, M., Pleva, M., Kiktová, E., Juhár, J. and Čižmár, A., “Efficient Acoustic Detector of Gunshots and Glass Breaking”, Multimedia Tools and Applications, Cilt 75, No 17, 10441-10469, 2016.
- İnternet: https://www.stillnorthmedia.com, 20.02.2017.
- İnternet: http://www.airbornesound.com, 20.02.2017.
- Rabiner, L. R. and Schafer, R. W., "Digital Processing of Speech Signals", Prentice Hall, (1978).
- Ertürk, S., "Sayısal İşaret İşleme", Birsen Yayınevi, İstanbul, (2002).
- Tarng, W., Chen, Y.-Y., Li, C.-L., Hsie, K.-R., and Chen, M., "Applications of support vector machines on smart phone systems for emotional speech recognition", World Academy of Science, Engineering And Technology, 72106–113 (2010).
- Bou-Ghazale, S. E. and Hansen, J. H., "A comparative study of traditional and newly proposed features for recognition of speech under stress", Speech And Audio Processing, IEEE Transactions On, 8 (4): 429–442 (2000).
- Loizou, P., "Colea: A MATLAB software-tool for Speech Analysis", University Of Arkansas, Arkansas, (2003)
- Boersma, P. P. G., “Praat, a System for Doing Phonetics by Computer”, Glot international, No 5, 2002.
- Eyben, F., Wöllmer, M., and Schuller, B., "Opensmile: The munich versatile and fast open-source audio feature extractor", Proceedings of the 18th ACM International Conference on Multimedia, Frenze, 1459–1462 (2010).
- Rezaei, N. and Salehi, A., "An antroduction to speech sciences (acoustic analysis of speech)", Iranian Rehabilitation Journal, 4 (4): 5–14 (2006).
- Sethu, V., "Automatic emotion recognition: An investigation of acoustic and prosodic parameters", Doktora Tezi, The University of New South Wales Electrical Engineering & Telecommunications, Sydney, (2009).
- Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. and Witten, I. H., ”The WEKA Data Mining Software: An update”, ACM SIGKDD Explorations Newsletter, Cilt 11, No 1, 10-18, 2009.
The Impact of Acoustic Parameters on the Classification of Firearms Using Gunshots
Year 2018,
, 462 - 469, 30.06.2018
Turgut Özseven
,
Muharrem Düğenci
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.
References
- 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.
- 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.
- 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.
- Arslan, Y. and Güldoğan, B., “Impulsive Sound Detection and Gunshot Recognition”, In Signal Processing and Communications Applications Conference, 511-514, May 2015.
- Maher, R. C., “Acoustical Characterization of Gunshots”, In Signal Processing Applications for Public Security and Forensics, 1-5, April 2007.
- 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.
- Özbal, E.A.,”Septum Devisyonlu Hastaların Septoplast Operasyonu Öncesi ve Sonrası Akustik Ses Analizi ile Değerlendirilmesi”, Uzmanlık Tezi, Şişli Etfal Hastanesi, İstanbul, 2008.
- Sataloff, R. T., “Treatment of Voice Disorders”, Plural Publishing, 2005.
- İnternet: http://www.raytheon.com/, 20.02.2017.
- 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.
- 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.
- 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.
- Djeddou, M. and Touhami, T., “Classification and Modeling of Acoustic Gunshot Signatures”, Arabian Journal for Science & Engineering, Cilt 38, No 12, 2013.
- Suman, P., Karan, S., Singh, V. and Maringanti, R., “Algorithm for Gunshot Detection Using Mel-frequency Cepstrum Coefficients (MFCC)”, In Proceedings of Ninth International Conference on Wireless Communication and Sensor Networks, 155-166, 2014.
- Lojka, M., Pleva, M., Kiktová, E., Juhár, J. and Čižmár, A., “Efficient Acoustic Detector of Gunshots and Glass Breaking”, Multimedia Tools and Applications, Cilt 75, No 17, 10441-10469, 2016.
- İnternet: https://www.stillnorthmedia.com, 20.02.2017.
- İnternet: http://www.airbornesound.com, 20.02.2017.
- Rabiner, L. R. and Schafer, R. W., "Digital Processing of Speech Signals", Prentice Hall, (1978).
- Ertürk, S., "Sayısal İşaret İşleme", Birsen Yayınevi, İstanbul, (2002).
- Tarng, W., Chen, Y.-Y., Li, C.-L., Hsie, K.-R., and Chen, M., "Applications of support vector machines on smart phone systems for emotional speech recognition", World Academy of Science, Engineering And Technology, 72106–113 (2010).
- Bou-Ghazale, S. E. and Hansen, J. H., "A comparative study of traditional and newly proposed features for recognition of speech under stress", Speech And Audio Processing, IEEE Transactions On, 8 (4): 429–442 (2000).
- Loizou, P., "Colea: A MATLAB software-tool for Speech Analysis", University Of Arkansas, Arkansas, (2003)
- Boersma, P. P. G., “Praat, a System for Doing Phonetics by Computer”, Glot international, No 5, 2002.
- Eyben, F., Wöllmer, M., and Schuller, B., "Opensmile: The munich versatile and fast open-source audio feature extractor", Proceedings of the 18th ACM International Conference on Multimedia, Frenze, 1459–1462 (2010).
- Rezaei, N. and Salehi, A., "An antroduction to speech sciences (acoustic analysis of speech)", Iranian Rehabilitation Journal, 4 (4): 5–14 (2006).
- Sethu, V., "Automatic emotion recognition: An investigation of acoustic and prosodic parameters", Doktora Tezi, The University of New South Wales Electrical Engineering & Telecommunications, Sydney, (2009).
- Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. and Witten, I. H., ”The WEKA Data Mining Software: An update”, ACM SIGKDD Explorations Newsletter, Cilt 11, No 1, 10-18, 2009.