Sound Parameter Estimation in a Security System

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1 INSTITUTE OF INFORMATION AND COMMUNICATION TECHNOLOGIES BULGARIAN ACADEMY OF SCIENCE Sound Parameter Estimation in a Security System I. Garvanov 1, Chr. Kabakchiev 2, V. Behar 3 1 University of Library Studies & Information Technologies, 1784 Sofia, Bulgaria igarvanov@yahoo.com 2 Sofia University St. Kliment Ohridski, 1164 Sofia, Bulgaria ckabakchiev@fmi.uni-sofia.bg 3 Institute of Information & Communication Technologies, BAS, 1113 Sofia, Bulgaria behar@bas.bg 7/15/213 1 /acomin

2 Outline Introduction Sensors and signals Signal Processing Simulation Results Conclusion 7/15/213 2

3 Introduction (1) The sensors used for protection are activated in the event of an adverse situation in the protected space. In case of fire, smoke, vibration, and breakage of glass or opening the car the sirensofsensorsgivealoudbeepforafewminutes. The assessment of the direction and parameters of the incoming sound signals can be used to guide the camera that records the situation in the most dangerous direction. The localization of sound signal direction could be used for pointing the additional video surveillance devices, which record the additional information and send it to control center for further analysis. Sound direction localization could be performed through: - parabolic microphones; - microphone arrays (linear, rectangular, circular); 7/15/213 3

4 Introduction (2) After the analysis of parameters of such sound signals that arrived from the detected directions the video cameras are directed in such directions, from where have been arrived the signals having the most important priority (emergency, alarm and warning). In modern security and surveillance systems, the operational control of protective and warning means is based on theanalysis of alarms received from different sensors installed in the observation area. In this paper we consider a situation where the operational control of a video camera is based on sound parameter estimation. 7/15/213 4

5 Introduction (3) The simulation scenario includes three sensors, which generate three types of signals -warning; -alarm; -emergency; -one source of natural noise (car). The parameters of three sound generators produced by three well-known companies SoniTron, E2S and Sensor Systems are used in simulation. The results obtained demonstrate that the sound parameter estimates are very close to real sensors parameters. The paper evaluates both the duration of the sound pulseand the signal frequency spectrum by using the FFT. 7/15/213 5

6 Introduction (4) In this paper, a possible signal processing algorithm is proposed for sound parameter estimation. We consider the case, when the sound source is located in the array s far-field, and the sounds generated by sound sources propagate through the air. After analysis of parameters of all signals received from the detected directions, a video camera is directed in such a direction, from where has been arrived the signal having the most important priority (emergency, alarm and warning). 7/15/213 6

7 Sensors and signals In this work are tested the signal generated by sensors of several companies SONITRON, E2S and SYSTEM SENSOR. The security sensors, using the mounted sirens, that generate special beeps, warn on abnormal situations that arise in the protected space, warning, alarm and danger. 7/15/213 7

8 Sensors and signals(1) Two main signal parameters of such sensors are the sound power and the sound frequency. Company Sound power [db] Sound frequency [Hz] SONITRON E2S 1 1 SYSTEM SENSOR Table 1: Sensors parameters 7/15/213 8

9 Sensors and signals(2) Depending on the non-normal situation the sensors emit different sound signals with the parameters. Table 2: Signal parameters Continuous (warning) Sensor signals Intermittent-I (alarm) Intermittent-II (emergency) f int = Hz f int = 5 Hz f int = 1 Hz T sig = 1 s T sig = 3 s T sig = 6 s The different devices generate various sound signals of type alarms. The sound signal Warning is a continuous harmonic signal with duration of 1s. The signal Alarm is an intermittent signal with the frequency of interruption of 5 Hz and duration of 3s (type-i). 7/15/213 9

10 Sensors and signals(3) sound signals of type warning, alarm and danger a) S O N I T R O N b) S Y S T E M c) E 2 S Interference (sound of a car) Microphone noise 7/15/213 1

11 Signal Processing (1) Other sound source Sensor B Sensor C Sensor A R B R C R A α B α A α C Microphone array (video camera) Many sensors for fire detection or building surveillance are equipped with sound alarm devices. In case of alarm event (smoke, flame, intrusion, glass breaking, and unauthorized car opening) the alarm device emits powerful sound signal with various duration. For the sake of simplicity, let s assume that a set of sensors and one microphone array are installed for the object protection in the observation area and a video camera can be located above a microphone array. 7/15/213 11

12 Signal Processing (2) x 1 x M... DOA Estimation Parameter Estimation Priority Estimation Analysis Video Camera Control Figure 5: Signal processing in a security system In a security system, the sound direction localization could be used for pointing the additional video surveillance devices (video cameras), which record the additional information and send it to control center of a security system. The priority direction for pointing of a video camera is estimated on the base of a parameter analysis of the signals received from the detected sound sources. The general block-scheme of signal processing in a security system is shown. 7/15/213 12

13 Signal Processing (3) DOA 8% mean (Env) Envelope FFT -1 db Figure 6: The block-scheme of sound parameters estimation Pulse Duration Estimation Frequency Estimation We assume that the adaptive beam pattern thresholding (CFAR) is performed and, finally, the direction of-arrival (DOA) estimates are found as directions where the local maximum exceeds an adaptive threshold. The duration of the estimation of the sound pulse is obtained after comparing the envelope of the signal with a threshold that is 8% of the average envelope. The estimation of the frequency of the sound signal is received again compared with a threshold value of -1 db (Fig. 6). 7/15/213 13

14 Simulation Results (1) The scenario includes: sensors (A, B and C) located respectively at 5m, 6m and 7m away from the microphone array. a car as a source of natural noise located at 9 m away from the microphone array. In the scenario of simulation, the azimuth of a car is zero relative to the microphone array. Power of the sound signal generated by a car is 11dB. Sensor A r Other sound source d LW = 11dB 5m Microphone array β A 9m β B D β C Sensor B 6m 7m Sensor (video camera) The computer simulation is performed in order to demonstrate the capability of the presented algorithm to estimate sound parameters. C 7/15/213 14

15 Simulation Results (2) Considered sound alarm devices are characterized with parameters : Sound power (LW),dB; Carrier frequency (Hz); Signal waveform (continuousharmonic, intermittentharmonic, increasing and decreasing chirp signal, constant); Number of different signals; Model 1. Company SONITRON (Belgium) Signal waveform Working Voltage Min, V Max, V Frequency Hz Pulse frequency Consumption SCI 535 A1 Multimode SCI 535 B1 Multimode SCI 535 A5 Multimode SCI 535 B5 Multimode Working temperature: o C Min, ma Max, ma Sound power db 7/15/213 15

16 Simulation Results (3) 2.Company SYSTEM SENSOR(USA) Hz Type 1 Screen Hz s Hz Type 2 Screen Hz s Combined multi alert home and strobes EMA24FRSSR LW=13 db 32 different sound signals 7/15/213 16

17 Simulation Results (4) 3.Company E2S (UK) LW=1 db 32 different sound signals 7/15/213 17

18 Simulation Results (5) Considered microphone arrays are characterized with parameters : configuration(linear, rectangular, square); number of microphones; frequency band[1 5] Hz; microphone noise 35dB Company Brüel&Kjær (Denmark ) 7/15/213 Microphone 4935 Microphone array WA 87 18

19 Simulation Results (6) Other sound source LW = 11dB Sensor A 9m Sensor B 6m Sensor C 5m β B 7m β A β C r d Microphone array D (video camera) The simulation scenario includes three sensors, which generate three types of signals (warning, alarm and emergency), and one source of natural noise (car). 7/15/213 19

20 Simulation Results (7) sound signals of type warning, alarm and danger Signal-1.1 Signal and noise Signal and noise.1 1 x 1-3 Envelope and threshold Envelope and threshold.1 db -5 Signal and noise spectrum frequency [Hz] Signal-2 Signal Signal and noise Envelope and threshold db db frequency [Hz] frequency [Hz] SoniTron signals, threshold and signals spectrum 7/15/213 2

21 Simulation Results (8) sound signals of type warning, alarm and danger Signal-1 Signal-2 Signal-3.2 Signal and noise Signal and noise Signal and noise Envelope and threshold Envelope and threshold Envelope and threshold db db db -5 Signal and noise spectrum frequency [Hz] frequency [Hz] frequency [Hz] E2S signals, threshold and signals spectrum 7/15/213 21

22 Simulation Results (9) sound signals of type warning, alarm and danger Signal-1 Signal-2 Signal-3.2 Signal and noise Signal and noise Signal and noise Envelope and threshold db Envelope and threshold Envelope and threshold.2.1 db db Signal and noise spectrum frequency [Hz] frequency [Hz] frequency [Hz] System Sensor signals, threshold and signals spectrum 7/15/213 22

23 Simulation Results (1).4 Interference and noise.6 Envelope and threshold -2 Interference and noise spectrum db x 1-3 Noise x 1-3 Envelope and threshold frequency [Hz] Noise spectrum db frequency [Hz] Interference signal generated by a car and the internal noise of a microphone noise of the microphone array. 7/15/213 23

24 Conclusions The presented algorithm enables to recognize the type of abnormal situations arisen in the area of observation in order to be taken the corresponding solutions for control of the security system. Thanks to the adaptive microphone array processing, the presence of interference signals does not influence significantly the determination of the signal parameter estimates. The obtained results demonstrate that the signals from different sensors can be estimated and there parameters are equal to real sensors parameters. 7/15/213 24

25 Acknowledgements This work is financially supported: by the Bulgarian Science Fund (projects DTK 2/28.29, DDVU 2/5/21) partly by the project AComIn "Advanced Computing for Innovation", grant 31687, funded by the FP7 Capacity Programme (Research Potential of Convergence Regions). 7/15/213 25

26 Reference 1. Benesty, J., Chen, J., Huang, Y., 28. Microphone array signal processing, Springer. 2. Godara, L., Application of antenna arrays to mobile communications, part II: beam-forming and direction-of-arrival considerations. In Proc. of the IEEE, vol.85, No 8, pp Ioannides, P., Balanis, C., 25. Uniform circular and rectangular arrays for adaptive beamforming applications. IEEE Trans. on Antenna. Wireless Propagation. Letters, vol.4., pp Trees, H., Van, L., 22.Optimum Array Processing. Part IV. Detection, Estimation, and Modulation Theory. New York, JohnWiley and Sons, Inc.. 5. Tummonery, L., Proudler, I., Farina, A., McWhirter, J., QRD-based MVDR algorithm for adaptive multi-pulse antenna array signal processing. In Proc. Radar, Sonar, Navigation, vol.141, No 2, pp Vouras, P., Freburger, B., 28. Application of adaptive beamforming techniques to HF radar. InProc. IEEE conf. RADAR 8, May, pp Behar V., Kabakchiev, Chr, Kyovtorov, V., 21. STAP Approach for DOA Estimation using Microphone Arrays, Signal Processing Workshop, SPW-21, June 21, Vilnius, Lithuania, SPIE Proceedings, vol. 7745, 77451J /15/213 26

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