IP Library Granted Patent US 11,317,233
Granted Patent B2
US 11,317,233 · App. 17/054,462 · Granted Apr 26, 2022

Acoustic program, acoustic device, and acoustic system

Inventors: Hideki Koike (Tokyo, JP); Homare Kon (Tokyo, JP)
Assignee: CLEPSEADRA, INC.
H04S7/303G06K9/6215G06T11/00G10K15/08H04S2400/15
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Quick Facts
Patent No.
US 11,317,233
App. No.
17/054,462
Granted
Apr 26, 2022
Kind
B2
Abstract

An acoustic device includes: an imaging device configured to take a sample image of a space as a sound field and create an image data on the space based on the taken sample image; a sound collector configured to collect a sound generated in the space or to collect a previously-collected acoustic data therein; and a computation part configured to previously compute a plurality of parameters relevant to a coefficient of spatial acoustic filter corresponding to the sample image of the space and previously learn a sound field model of the space shown in the sample image. The computation part is configured to construct a sound field model of the sample image taken by the imaging device or of a previously-taken sample image, from the acoustic data collected by the sound collector, using the coefficient of spatial acoustic filter.

Claims (44)

1. An acoustic device, comprising:

an imaging device configured to take a sample image of a space as a sound field and create an image data on the space based on the taken sample image;

a sound collector configured to collect a sound generated in the space or to collect a previously-collected acoustic data therein; and

a computation part configured to previously compute a plurality of parameters relevant to a coefficient of spatial acoustic filter corresponding to the sample image of the space and previously learn a sound field model of the space shown in the sample image,

wherein the computation part is configured to construct a sound field model of the sample image taken by the imaging device or of a previously-taken sample image, from the acoustic data collected by the sound collector, using the coefficient of spatial acoustic filter.

2. The acoustic device according to claim 1 ,

wherein the computation part is configured to: estimate a coefficient of spatial acoustic filter of an image of an unknown space, using the sound field model of the previously-learned sample image; and construct a sound field model of the unknown image, using the estimated spatial acoustic filter coefficient.

3. The acoustic device according to claim 2 ,

wherein the unknown image is an image of at least one of a pinna of an ear and a canal thereof, and

wherein the computation part is configured to estimate a coefficient of spatial acoustic filter of the ear, based on the image.

4. An acoustic device, comprising:

an imaging device configured to take a sample image of a space as a sound field or to collect an image data on a previously-taken sample image thereof;

a sound collector configured to collect a sound generated in the space or to collect a previously-collected acoustic data therein; and

a computation part configured to previously construct a sound field model of the sample image taken or collected by the imaging device, based on the acoustic data collected by the sound collector, using a coefficient of spatial acoustic filter,

wherein the computation part is configured to estimate a coefficient of spatial acoustic filter of an image of an unknown space, using the previously-constructed sound field model of the sample image.

5. An acoustic device, comprising:

an imaging device configured to take a sample image of a space as a sound field or to collect an image data on a previously-taken sample image; and

a computation part configured to construct a sound field model of the sample image taken or collected by the imaging device, using a coefficient of spatial acoustic filter,

wherein the computation part is configured to superimpose either a previously-taken image, or an image created by computing the previously-taken image in the computation part, on the image taken by the imaging device.

6. The acoustic device according to claim 1 ,

wherein the computation part is configured to estimate, upon input of an image data and an acoustic data, a coefficient of spatial acoustic filter relevant to the inputted image data; and output the inputted acoustic sound with a reverberation characteristic obtained based on the coefficient of spatial acoustic filter added thereto.

7. The acoustic device according to claim 6 ,

wherein the acoustic data is a dubbed-in voice of video contents.

8. The acoustic device according to claim 1 , further comprising an acoustic output device configured to output an acoustic sound with a reverberation characteristic added thereto.

9. The acoustic device according to claim 1 ,

wherein the image data is an image data of a moving image, and the spatial acoustic filter coefficient is estimated using a difference between a frame in the moving image and a background image in the frame or between a current frame and a preceding frame.

10. The acoustic device according to claim 1 ,

wherein a plurality of the imaging devices: are connected to a cloud in which an acoustic program is constructed; and collects an image data and an acoustic data from the program.

11. The acoustic device according to claim 1 ,

wherein a plurality of the imaging devices: are connected to a cloud in which an acoustic program is constructed; and collects an image data and an acoustic data from the program, and, in the acoustic program, a sound field model of a space is learned and a spatial acoustic filter coefficient thereof is estimated in a neural network convoluted in multiple stages.

12. The acoustic device according to claim 4 ,

wherein the computation part is configured to estimate, upon input of an image data and an acoustic data, a coefficient of spatial acoustic filter relevant to the inputted image data; and output the inputted acoustic sound with a reverberation characteristic obtained based on the coefficient of spatial acoustic filter added thereto.

13. The acoustic device according to claim 4 , further comprising an acoustic output device configured to output an acoustic sound with a reverberation characteristic added thereto.

14. The acoustic device according to claim 4 ,

wherein the image data is an image data of a moving image, and the spatial acoustic filter coefficient is estimated using a difference between a frame in the moving image and a background image in the frame or between a current frame and a preceding frame.

15. The acoustic device according to claim 4 ,

wherein a plurality of the imaging devices: are connected to a cloud in which an acoustic program is constructed; and collects an image data and an acoustic data from the program, and, in the acoustic program, a sound field model of a space is learned and a spatial acoustic filter coefficient thereof is estimated in a neural network convoluted in multiple stages.

16. The acoustic device according to claim 5 ,

wherein the computation part is configured to estimate, upon input of an image data and an acoustic data, a coefficient of spatial acoustic filter relevant to the inputted image data; and output the inputted acoustic sound with a reverberation characteristic obtained based on the coefficient of spatial acoustic filter added thereto.

17. The acoustic device according to claim 5 , further comprising an acoustic output device configured to output an acoustic sound with a reverberation characteristic added thereto.

18. The acoustic device according to claim 5 ,

wherein the image data is an image data of a moving image, and the spatial acoustic filter coefficient is estimated using a difference between a frame in the moving image and a background image in the frame or between a current frame and a preceding frame.

19. The acoustic device according to claim 5 ,

wherein a plurality of the imaging devices: are connected to a cloud in which an acoustic program is constructed; and collects an image data and an acoustic data from the program, and, in the acoustic program, a sound field model of a space is learned and a spatial acoustic filter coefficient thereof is estimated in a neural network convoluted in multiple stages.

Assignments (2)
CHANGE OF ADDRESS OF THE ASSIGNEE Recorded Jan 21, 2022
From: CLEPSEADRA, INC.
To: CLEPSEADRA, INC.
Reel/Frame 058803/0032 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 11, 2020
From: KOIKE, HIDEKI; KON, HOMARE
To: CLEPSEADRA, INC.
Reel/Frame 054339/0593 →
Priority Claims (1)
JP JP2018-092622 · May 11, 2018 · national
Continuity (1)
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