Room acoustics simulation using deep learning image analysis

1. A method comprising:

receiving an image of a real-world environment;

using a machine learning classifier, trained using training images of real-world environments labeled with one or more room types and training acoustic presets derived from each of the one or more room types, each training acoustic preset having acoustic parameters for sound reverberation including a reverberation decay time, classifying the image to the one or more room types and the acoustic presets associated with the one or more room types, wherein each acoustic preset includes the acoustic parameters for the sound reverberation including the reverberation decay time;

selecting an acoustic preset among the acoustic presets; and

performing an acoustic environment simulation based on the acoustic parameters of the acoustic preset by modeling the sound reverberation for one or more virtual sound objects placed virtually in the real-world environment based on the acoustic parameters of the acoustic preset including the reverberation decay time.

2. The method of claim 1 , further comprising:

using the machine learning classifier, classifying the image, or one or more further images, to produce one or more acoustic parameter modifiers; and

modifying the acoustic parameters of the acoustic preset based on the one or more acoustic parameter modifiers, to produce a modified acoustic preset including modified acoustic parameters for the acoustic environment simulation,

wherein performing the acoustic environment simulation includes performing the acoustic environment simulation using the modified acoustic parameters.

3. The method of claim 1 , wherein:

the acoustic parameters further include one or more of a frequency dependent decay time, a room size, a reflection delay, an early onset time, and a diffusion level.

4. The method of claim 1 , wherein classifying includes classifying the image directly to the acoustic presets, without performing an intervening classification or operation.

5. The method of claim 1 , wherein:

classifying includes classifying the image to the acoustic presets such that the acoustic presets have respective confidence levels; and

selecting includes selecting the acoustic preset such that the acoustic preset is associated with a highest one of the respective confidence levels.

6. The method of claim 5 , further comprising:

receiving a second image of the real-world environment;

using the machine learning classifier, classifying the second image to produce second acoustic presets that have respective second confidence levels;

determining whether one or more of the respective second confidence levels exceeds a confidence level threshold; and

if one or more of the respective second confidence levels exceeds the confidence level threshold, selecting a second acoustic preset among the acoustic presets, and replacing the acoustic preset with the second acoustic preset for the acoustic environment simulation.

7. The method of claim 6 , further comprising:

if one or more of the respective second confidence levels do not exceed the confidence level threshold, not selecting the second acoustic preset, and not replacing the acoustic preset for the acoustic environment simulation.

8. The method of claim 1 , wherein receiving the image of the real-world environment includes receiving the image as a 360 degree image of the real-world environment.

9. The method of claim 1 , wherein the image represents a composite of different images.

10. The method of claim 1 , wherein the acoustic parameters of the acoustic presets were derived algorithmically from acoustic impulse responses representative of the real-world environments represented in the training images on which the machine learning classifier was trained.

11. The method of claim 1 , wherein the machine learning classifier was trained on the training images of the real-world environments labeled with respective ones of the acoustic presets that most likely match the real-world environments.

12. The method of claim 1 , further comprising:

receiving a digital three-dimensional (3D) mesh (3D mesh); and

estimating acoustic properties of physical materials represented m the 3D mesh, to produce early reflection parameters for the acoustic environment simulation.

13. An apparatus comprising:

a processor configured to:

receive an image of a real-world environment;

use a machine learning classifier including one or more neural networks, trained on training images of real-world environments labeled with one or more room types and training acoustic presets derived from each of the one or more room types, each training acoustic preset having acoustic parameters for sound reverberation including a reverberation decay time, to classify the image to the one or more room types and the acoustic presets associated with the one or more room types, wherein each acoustic preset includes the acoustic parameters for the sound reverberation including the reverberation decay time;

select an acoustic preset among the acoustic presets; and

perform an acoustic environment simulation based on the acoustic parameters of the acoustic preset including the reverberation decay time.

14. The apparatus of claim 13 , wherein the processor is configured to perform the acoustic environment simulation by modeling the sound reverberation for one or more virtual sound objects placed virtually in the real-world environment based on the acoustic parameters of the acoustic preset.

15. The apparatus of claim 14 , wherein the processor is further configured to:

use the machine learning classifier to further classify the image to produce one or more acoustic parameter modifiers;

modify the acoustic parameters of the acoustic preset based on the one or more acoustic parameter modifiers, to produce a modified acoustic preset including modified acoustic parameters for the acoustic environment simulation; and

further perform the acoustic environment simulation using the modified acoustic parameters.

16. The apparatus of claim 13 , wherein:

the acoustic presets have respective confidence levels; and

the processor is configured to select by selecting the acoustic preset that has a highest one of the respective confidence levels.

17. The apparatus of claim 13 , wherein the machine learning classifier was trained on the training images of the real-world environments labeled with respective ones of the acoustic presets that most likely match the real-world environments.

18. A non-transitory computer readable medium encoded with instructions that, when executed by a processor, cause the processor to:

receive an image of a real-world environment;

use a machine learning classifier, previously trained on training images of real-world environments labeled with one or more room types and training acoustic presets derived from each of the one or more room types, each training acoustic presets including acoustic parameters that represent sound reverberation including a reverberation decay time, to classify the image directly to the one or more room types and the acoustic presets associated with the one or more room types, each including the acoustic parameters that represent the sound reverberation including the reverberation decay time;

select an acoustic preset among the acoustic presets; and

perform an acoustic environment simulation based on the acoustic parameters of the acoustic preset.

19. The non-transitory computer readable medium of claim 18 , wherein the instructions to cause the processor to perform the acoustic environment simulation include instructions to cause the processor to model the sound reverberation for one or more virtual sound objects placed virtually in the real-world environment based on the acoustic parameters of the acoustic preset including the reverberation decay time.

20. The non-transitory computer readable medium of claim 19 , further comprising instructions to cause the processor to:

use the machine learning classifier to further classify the image to produce one or more acoustic parameter modifiers;

modify the acoustic parameters of the acoustic preset based on the one or more acoustic parameter modifiers, to produce a modified acoustic preset including modified acoustic parameters for the acoustic environment simulation; and

further perform the acoustic environment simulation using the modified acoustic parameters.