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US 12,112,521 B2Granted
Room acoustics simulation using deep learning image analysis
AssigneeDTS, Inc.
InventorsMartin Walsh, Aoife McDonagh, Michael M. Goodwin, Edward Stein, Peter Corcoran
Life of this patent
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Abstract
A method comprises: receiving an image of a real-world environment; using a machine learning classifier, classifying the image to produce classifications associated with acoustic presets for an acoustic environment simulation, the acoustic presets each including acoustic parameters that represent sound reverberation; and selecting an acoustic preset among the acoustic presets based on the classifications.
Claims20
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 more elements
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.
3 more elements
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.
2 more elements
Dependencies are inferred from the claim text.