Computational acoustic vision by solving phase ambiguity confusion

Abstract

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.