NEURAL NETWORKS APPLIED TO GUN AND AMMMUNITION RECOGNITION FROM SHOOTING SOUNDS A. Glez. Arrieta, L.Alonso R., A. L. S. Lázaro , J.R. García and J. M. del Amo Rodríguez Departamento de Informática y Automática, Facultad de Ciencias. Universidad de Salamanca. Plaza de la Merced, s/n. (37008) Salamanca (Spain). e-mail:
[email protected]
ABSTRACT In this work a system for the automatic recognition of the gun and ammunition used in a shooting is presented. The system is based on statistical functions and neural networks and it is developed under a graphical user interface (GUI). INTRODUCTION The recognition of sound patterns by means of neural networks has been succesfully applied to fields such as speech and speaker recognition, acoustic-phonetic transcription, and so on. (Beale and Jackson, 1990), (Freeman and Skapura, 1993), (Furui, 1991). Nevertheless, there exist some applications of a relevant economic or social interest where neural networks have been seldom applied. One of these areas is the identification of guns and munition based on the shooting sound (Macía et al., 1996). This applications, if succesful, could be applied in security systems, in conjuction with the video cameras. In this work a first approach to the problem is presented, based on a data set limited to a few guns (five) and munition types (two). Data recording was performed on two very different environments: in a closed room, an indoor shooting gallery and in a open space, an outdoor shhoting gallery , both at the Police School in Avila (Spain). These two environments were chosen in an attempt to
simulate the actual situations of the eventual application of the system. METHODS Data adquisition As a starting point, we used five different gun classes: Star gun, Astra revolver, Police shotgun , Nato rifle and Cetme (Macía et al., 1996). For each class, we used two ammunition types, and worked on two environments: indoor and outdoor shooting galleries. For each case, 10 shootings were performed. Since the objective is a gun independent identification, we used three different guns of each class. In this way, we have a data base of approximately 600 identified, and labelled, shot sounds. The sounds recordings, shootings in this particular case, were made on a digital minidisc using a high fidelity microphone. From the minidisc, the information was transferred to the computer through a standard sound card and the appropriate software. Data base With the samples so adquired, an analysis in time and frequency domain was performed. An edge detector was designed, to separate the shot sound from the background noise in an automatic way. Then some frequency characteristics were computed, working with different windows on the time domain.
Escuela General de Policía, C/ Carretera de Madrid s/n. Ávila (España).
The computed characteristic were: -
Frequency in Hz that divides the Fourier transform area into two equal parts. Maximum energy value. Frequency in Hz for the maximum energy. Formants energy and frequency. Normalized energy for spectral bands. Spectral density on time domain..
On Fig. 1, a sample in the time domain and its Fourier transform is shown: Figure 3. The band normalized energy for the revolver.
Figure 1. Analysis in time and frequency. Data analysis Once the parameters were extracted, a detailed study was performed, to select the most influential of them in the class discrimination task. As a result, the band normalized energy makes it possible to discriminate quite exactly between guns, as it is shown on Fig.2, 3 and 4.
Figure 2. The band normalized energy for the pistol
Figure 4. The band normalized energy for the shotgun. In the same way, working with several windows in time domain, a frequency spectrum is obtained that allows us to discriminate not only the guns but the munition as well. On Fig. 5 and 6, the results with 12 Hamming windows of 2048 points for two different guns are shown.
Star gun Astra revolver Police shotgun
% Recognition rate 98,6 96,7 97,8
The work is being implemented on a SUN Workstation, by using Matlab Neural Network Toolbox and its GUI to design a friendly, functional and simple environment for the identification and information of the gun and munition (Krauss et al., 1994), (Hanselman y Littlefield, 1996). Figure 5. Results for the Star pistol, with 9 mm P. munition, Semiblindado, in the indoor gallery.
The Fig.7,.8 and .9 show some of the application windows:
Figure 7. Shootings menu.
Figure 6. Results for the Astra revolver and .38 munition, Special Semiblindado in the indoor gallery. Recognition
Figure 8. Results menu.
The pattern recognition techniques that were used are the classical methods of classifier functions and neural networks, mainly the Multilayer Perceptron with the Backpropagation learning algorithm, that produced a high recognition rate. We show the results of the recorded shootings in a previous analysis. The experiments were made with three different types of weapons in a shooting gallery.
Figure 9. Presentation of results.
Future works Tests with some other neural networks paradigms are been performed. Next step will be to enlarge the data base with more guns, munitions and shooting environments, and the use of Fuzzy Logic based systems to the classification task. CONCLUSIONS Neural networks allow the discrimination, with a low error rate, between guns and munitions, based on the recorded shot sound, with a small number of classes. The final objective is to allow high recognition rates for a number of classes (arms, munitions and shooting environments) big enough so as to allow the use of the method as part of an actual, robust and simple, security system. REFERENCES Beale & Jackson; “Neural Computing. An introduction”, Adam Hilger (1990). Freeman J. A. y Skapura D. M.; “Redes neuronales. Algoritmos, aplicaciones y técnicas de programación”, AdisonWesley (1993). Furui, S.; “Digital Speech Processing, Synthesis, and Recognition”, Marcel Dekker, Inc. (1991). Hanselman, D. y Littlefield, B. (Trac. Dormido Bencomo S. y otros); “Matlab”, The Math Works Inc. (1996). Krauss, Thomas P.; Shure L. & Little J. N.; “Signal Processing TOOLBOX For Use with MATLAB”, The MATH WORKS Inc. (1994). Macía, M. J.; Espada, R.; del Amo, J.; Lafont, L.; Herrero, F. y Castilla, M.; “Tiro, Armas y Explosivos”, Dirección General de la Policía. División de Formación y Perfeccionamiento (1994). Macía, M. J.; del Amo, J.; Herrero, F.; “Cuaderno de Formación nº 13. Manual de Tiro, Armas y Explosivos”, Dirección General de la Policía. División de Formación y Perfeccionamiento (1996).
Martín del Brío, B. y Sanz, A.; “Redes Neuronales y Sistemas Borrosos”, Ra-Ma (1997). Ríos, J.; Pazos A.; Brisabosa, N. R.; Caridad S.; “Estructura dinámica y aplicaciones de las redes de neuronas artificiales”, Centro de Estudios Ramón Areces (1991). Sigfried, F. Hübner; “Tiros de combate y defensa personal. Nuevas técnicas de tiro de Policía”, A.D.S Rede (1984).
