The localization of a single acoustic source on the horizontal plane using phase difference spectrum images is discussed. The azimuth for source is identified from the general linear relationship, which is extracted from the measured phase difference spectrum after filtering. The phase difference spectrum is introduced as the quasi-stationary cross-spectral phase between the sound signals detected simultaneously by two sensors. Acoustic source localization in an anechoic chamber having a metal base plate using two types of sound signals, white noise and voice, indicated that the phase difference spectrum was not affected with respect to the sound pressure level but was affected with respect to the azimuth for source. Although the phase difference spectrum measured in a reverberant room had less continuity as a function of frequency, a linear distribution of the images obtained from the data (dots) was observed on the frequency -phase difference plane. Using the phase difference spectrum images, the azimuths for various sources, which radiated any kind of broadband sound on separated time schedule, were precisely identified even in the reverberant room.
''Phase ambiguity'' leads to confusion in computational source localization where multiple source locations are introduced from a cross-spectral phase value measured by two sensors at high frequencies, where sound wavelength is shorter than sensor interval. In this paper, a frequency domain algorithm for broadband source localization by two sensors is proposed for solving ''phase ambiguity'' confusion under actual conditions. Using the overlapped and averaged phase differences of the cross-spectral phases measured over the audible frequency range, multiple source azimuths are identified from each phase difference as much as possible over the azimuth range of AE90. The frequency-independent azimuths extracted from multiple azimuths by Hough transformation provide the target source azimuths. The azimuths for the two loudspeakers can be identified simultaneously in this way from the phase differences measured over the full audible frequency range within an error of approximately 6 under reverberative conditions. By removing the numerical noise during source azimuth identification, the estimated source distribution corresponds to the diameters of the loudspeakers. When it is necessary to distinguish between near or far sound sources around the microphones, the horizontal azimuths for the sources can be precisely identified from all directions except at approximately AE90 if judgment of the front or back is given.
Computational acoustic vision by solving phase ambiguity confusion (CAVSPAC) is proposed for two-dimensional colorful imaging such as pointillisme in the broadband sound environment. The 2D distributions of equivalent point sources were identified as an image from the cross-power spectral phases of sound pressure measured by two pairs of microphones. Each point source was assigned a color corresponding to its frequency. Multiple source locations are introduced from one cross-spectral phase value because of ''phase ambiguity'' at high frequencies, when the microphone interval is wider than the sound wavelengths. The true source location was extracted from multiple source locations as being the frequency independent. The broadband noise source was visualized with a single two-way loudspeaker set at various positions in the reverberative room. Using CAVSPAC, the 2D image could be identified for the broadband sound source from all directions spherically, except in the area just beside, above and under the microphones. The moderate wider microphone interval than the sound wavelengths led to a better resolution at the source image.
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