DEVELOPMENT OF NOISE SOURCE IDENTIFICATION SYSTEM WITH MULTI-MICROPHONE SOUND SIGNAL PROCESSING ENHANCED BY VISUAL IMAGE PROCESS : A study on decrease of reflection influence

Kamiakito, Noboru; Nogami, Hidekazu; Tominaga, Daisuke; Yamashita, Yasuhiro; Zaima, Takefumi; Owaki, Masanao; Sugiyama, Takeshi; Wada, Hiroo

doi:10.3130/aija.68.1_2

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…This algorithm requires an iterative process for identifying both the incident signals and their positions. Kamiakito et al have shown that the point source distribution can express the broadband sound environment as an image based on the ray-trace model, in which each point source represents one sound source direction at a single frequency [11]. Each point source was localized using the crosspower spectral phase at that frequency, and the microphone interval was set shorter than the sound wavelength to avoid ''phase ambiguity.''…”

Section: Introductionmentioning

confidence: 99%

Computational acoustic vision by solving phase ambiguity confusion

Shimoyama

Yamazaki

2009

Acoust. Sci. & Tech.

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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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Section: Introductionmentioning

confidence: 99%

Computational acoustic vision by solving phase ambiguity confusion

Shimoyama

Yamazaki

2009

Acoust. Sci. & Tech.

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Blind Source Separation and Sound Source Localization on Time-Frequency Domain Considering Time Lag Information

Sasaki¹,

Suzuki²,

Yasuoka³

et al. 2009

Nihon Kenchiku Gakkai Kankyokei Ronbunshu

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The blind source separation and specification of location on time-frequency domain considering time lag information are conducted. The formulation based on the independency of time-frequency domain is presented. First of all, the number of source signals is specified. Next, the locations of source signals are specified. Then, the source signals are obtained. The numerical test is conducted to confirm our method, and then the locations of source signals and source signals are obtained by high accuracy.

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DEVELOPMENT OF NOISE SOURCE IDENTIFICATION SYSTEM WITH MULTI-MICROPHONE SOUND SIGNAL PROCESSING ENHANCED BY VISUAL IMAGE PROCESS : A study on decrease of reflection influence

Cited by 2 publications

References 3 publications

Computational acoustic vision by solving phase ambiguity confusion

Computational acoustic vision by solving phase ambiguity confusion

Blind Source Separation and Sound Source Localization on Time-Frequency Domain Considering Time Lag Information

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