# ACOUSTIC SIGNALS PROCESSING AND APPLIANCE FOR THE PROBLEM OF TRAFFIC FLOW MONITORING

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4 4. Development of Moving Road Vehicles Algorithm Detections Using Digital Processing for Its Acoustic Signals To develop the algorithm for detecting road vehicles was used the real acoustic signal recorded from the streets of Riga (Fig. 2). Figure 2. Example of acoustic signal In areas where the signal increases and then decreases, there is passing road vehicles, after that these areas are cutting out and using for calculations. During the experiment, acoustic signals were recorded under different weather conditions (Fig. 3-6). Analysing the Figures 3-6 we can conclude that each signal has its own specific form. Road vehicles have a common feature: the increasing signal, and then gradually decreasing. At last acoustic signal is clearly observed chaotic bursts indicating the noise which should be filtered. All these signals brings together that their maximum is attained at the points from.4 to 1. Moreover, the maximum mark of the road vehicle is in the range from.5 to.6 and.5 to 1 for wind x 1 5 Figure 3. Car signal x 1 5 Figure 4. Signal of public transport x 1 5 Figure 5. Car signal at rain x 1 5 Figure 6. Wind signal Each record was divided into fragments of 5 seconds length, since all the pulses of signals from vehicles travelling at a speed of about 6 km / h, fit in this time period (Fig. 2). The peculiarity of 7

5 acoustic recording is almost three times the redundancy of the sampling frequency: quantization and recording occur with a maximum sampling rate of 441 Hz and a microphone used in the experiment has a bandwidth of only 16 Hz. To eliminate redundancy and reduce the processing time the quantization of the signal has been carried out with a frequency corresponding to the conditions of registration (microphone), i.e. 16 khz. The result has been obtained as discrete sequence of 8 samples length. Then the signal was subjected to decimation, where every tenth sample was kept, retaining the basic properties of the signal unchanged, resulting in a signal of 8 samples length. On the basis of numerous of experiments it is established the optimum in terms of minimum-time of data processing, as the acoustic signal processing algorithm. It consists of 3 stages: Filtering in the spectral domain [6] for lower and upper frequencies of the acoustic signal using the function developed by filtering (x, lb, ub) in the Matlab software. The parameters of function are: x input array of points of the signal, lb lower limit of the filter, ub upper limit of the filter; Square power of the filtered signal; Calculation of the moving average of the window length 8 points, with a given step 1 samples with the help of the developed functions mean_vals (x, winsize, step) in the environment of Matlab. The parameters of function are: x input an array of the signal, winsize the length of the window, step step. The example of block diagram of an acoustic signal of the car (Fig. 7) and wind (Fig. 8) phase-out processing is presented below. Figure 7. Block-scheme of acoustic car signal processing 8

6 Figure 8. Block-scheme of acoustic wind signal processing After performing such operations the ratio between the signal of wind and the signal of the car has very low settings, though the pulse of the wind signal before the processing is several times greater. Maximum of signal of the car is now 3 times higher than the highest point of the wind that makes it possible to state certainly the presence or absence of a vehicle in the segment of the transport network, i.e. use the elementary amplitude detector. We must pay attention to the fact that the signals have been registered in the absence of pedestrians on the free stretch of road one-way traffic, where vehicles are moving at the maximum permissible speed. The effects of extraneous sources of sound, motion with low velocity dense mass of vehicles in traffic bottleneck and the noise from vehicles in opposite directions these all are the factors of effectiveness of the acoustic method, which is expected to detail study in future. 5. Conclusions As the result of studies it has been found that the acoustic method can be used for detecting of moving vehicles, therefore, the algorithm for determining the moving vehicles based on digital processing of acoustic signals is useful for surveillance. The advantages of this method are as follows: 9

7 Low cost of equipment, what helps to minimize the financial cost for full-scale implementation; Autonomy of the system; Mobility and flexibility of the system; Ability to work in all weather conditions. Unfortunately, this method is working well on the road with one lane in only one direction at absence of interferences. The most perspective creation of mobile and low-cost measurement system is a combination of acoustic sensors, longitudinal and transverse directions with observation data from WEBcamera making an additional influence research of various interferences. Also, the entire system must be completely manipulated (with the possibility of full control), which requires additional studies to determine the necessary protection level of data and all transmission channels. References 1. Kabashkin, I. Transport Telematics. Riga: Riga Aviation University, p. 2. Le Chevalier, Francois. Principles of Radar and Sonar Signal Processing. Artech House, p. 3. Kovalev, Vladimir A., Eichinger, William E. Elastic Lidar: Theory, Practice, and Analysis Methods. Wiley, p. 4. Klein, Lawrence A. Sensor Technologies and Data Requirements for ITS Applications. Artech House, p. 5. Gay, Steven L., Benesty, Jacob. Acoustic Signal Processing for Telecommunication. Springer, p. 6. Ingle, Vinay K., Proakis, John G. Digital Signal Processing Using MATLAB. Thomson Learning, p. 1

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