# Fast Sound Source Localization Based on SRP-PHAT Using Density Peaks Clustering
## 6 Real Environment Test  
Video-conferencing has been widely used in various fields. In a long-distance video-conference a speaker needs to be localized in real time for the convenience of capturing his/her face by camera. At present, this often relies on labor or half-linkages to control the camera, which has unsatisfactory efficiency and is labor consuming. Therefore, an automatic control system is necessary to be developed, in which sound source localization is the key. A series of tests in a real environment were conducted to verify the efficiency of ODB-SRP-PHAT. A conference room with the dimensions of 8.7 m \(\times\) 7.5 m \(\times\) 2.8 m in a university was chosen as the test field, in which a round table had 18 seats as shown in Figure 8. A circular microphone array consisting of eight omnidirectional microphones (MPA416) with a radius of 10 cm was placed at the center of the round table, at the height of 2.2 m as shown in Figure 8. The reverberation time and the averaged SNR were approximately 0.4 s and 16 dB, respectively, by estimation. At the stage of offline database construction, a sound source was, in turn, set at the center of each seat and at the height of 1.2 m. The sound signals, i.e., a piece of music generated by a blue-tooth speaker controlled by a mobile phone, emitted from each sound source were recorded by the microphone array three times with the sampling rate of 20 KHz. A Hanning window was used to convolute the sound signals and the windowed signals were divided into frames, with a frame length of 512 points and a frame shift of 256 points. There were 1080 frames for each signal, in which 200 frames with a larger short-time energy were selected for sound source localization using SRP-PHAT. The obtained 3600 sample points are shown in Figure 9.  
Then, DPC was used to deal with the sample points and, finally, 18 clusters were formed. The centers of the clusters were stored in the ODB. As shown in Figure 10 and Table 4, the azimuth space between any two adjacent sound sources was approximately 20\({}^{\circ}\), which was essentially consistent with the actual situation. There were deviations for sound source elevation and the dispersion was relatively big. This was partly due to the relatively low elevation resolution of the circular microphone array placed horizontally. In addition, the plate mounted with the microphone array tilted to some degree and was difficult to align. However, since the plate of the microphone array was kept at the same location and tilting status for both the offline database construction and the subsequent online localization, the results were not greatly affected. At the online localization stage, a person of 1.7 m high sat on the 18 seats in turn and counted from 1 to 10, repeated three times at each seat. With the sampling rate at 20 kHz, the sound signals collected by the microphone array were divided into frames and convoluted with a Hanning window. The frame length was 512, and the frame shift was 256. Among the frames collected at each seat, the first 100 frames with a larger short-time energy were used with SRP-PHAT and ODB-SRP-PHAT. For SRP-PHAT, the position with the largest value of SRP was judged as the sound source position. The tolerance was set as 10\({}^{\circ}\) and 20\({}^{\circ}\) for both the azimuth and elevation. If the deviation was smaller than the tolerance, the localization was considered successful. ODB-SRP-PHAT only calculated the SRP values at the 18 points stored in the ODB. If the largest value of the SRP was consistent at the point with the sound source position, it was a successful localization. The results of the localization success rate \(V\) are plotted in Figure 11.  
Figure 8: Layout and actual view of the meeting room. Figure 9: Sample points of the localization results (18 positions). Figure 11: Comparison of the analysis results between SRP-PHAT and ODB-SRP-PHAT.