Patent ID: 11937073
Assignee: AUDIOFOCUS, INC
Field: Computer technology (Electrical engineering)
Classification: CPC H  G | IPC G  H

Claim 14:
15. A method comprising:
generating a virtual n-dimensional space that includes one or more positions of one or more source nodes and a position of a receiver node;
executing, by a computer, a plurality of simulations including simulating acoustic signals emanating from the one or more source nodes within the virtual n-dimensional room;
estimating, for each of the plurality of simulations, a measure of the acoustic signals received at the receiver node;
computing a plurality of acoustic signal data samples based on the estimation for each of the plurality of simulations, wherein:
the one or more source nodes include a source node producing desired acoustic signals at the position of the receiver node,
a desired subset of the plurality of acoustic signal data samples includes acoustic data samples of sounds the receiver node desires to hear,
the one or more sources nodes include a source node producing interferer acoustic signals interfering with the desired acoustic signals,
an interferer subset of the plurality of acoustic signal data samples includes acoustic data samples of sounds interfering with the desired acoustic signals; and

creating a training data corpus for training an artificial neural network, the training data corpus comprising at least a sampling of the plurality of acoustic data samples, and the artificial neural network, once trained, is configured to generate an inference indicating a likely intended sound to a target receiver of a mixture of acoustic signals that include sounds directed toward the target receiver and sounds interfering with the sounds directed toward the target receiver, wherein creating the training data corpus includes:
sampling acoustic data samples from one or more source nodes of the one or more source nodes that are positioned within a predetermined distance of the position of the receiver node to form the desired subset, and
sampling acoustic data samples from one or more source nodes of the one or more source nodes that are positioned beyond the predetermined distance of the position of the receiver node to form the interfering subset; and

wherein the artificial neural network, once trained using the training data corpus, comprises a proximity-based enhancement artificial neural network that enables an enhancement of nearby speech while suppressing far-away speech.