US 12,112,521 B2
Room acoustics simulation using deep learning image analysis
Martin Walsh, Calabasas, CA (US); Aoife McDonagh, Calabasas, CA (US); Michael M. Goodwin, Calabasas, CA (US); Edward Stein, Calabasas, CA (US); and Peter Corcoran, Calabasas, CA (US)
Assigned to DTS Inc., Calabasas, CA (US)
Filed by DTS, Inc., Calabasas, CA (US)
Filed on Jun. 22, 2021, as Appl. No. 17/354,668.
Application 17/354,668 is a continuation of application No. PCT/US2019/066315, filed on Dec. 13, 2019.
Claims priority of provisional application 62/784,648, filed on Dec. 24, 2018.
Prior Publication US 2022/0101623 A1, Mar. 31, 2022
Int. Cl. G06V 10/764 (2022.01); G01H 7/00 (2006.01); G06V 20/20 (2022.01); G06V 20/64 (2022.01)
CPC G06V 10/764 (2022.01) [G01H 7/00 (2013.01); G06V 20/20 (2022.01); G06V 20/64 (2022.01)] 20 Claims
OG exemplary drawing
 
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.