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

Claim 0:
1. A method of synthesizing acoustic data signals for configuring an acoustics-enhancing machine learning model, the method comprising:
generating a virtual three-dimensional room that includes one or more positions of one or more sources of sound and a position of a receiver of sound;
executing, by a computer, a plurality of simulations including simulating acoustic signals emanating from the one or more positions of the one or more sources of sound within the virtual three-dimensional room;
estimating, for each of the plurality of simulations, a measure of the acoustic signals received at the position of the receiver of sound;
computing a plurality of acoustic signal data samples based on the estimation for each of the plurality of simulations, wherein:
the one or more sources of sound include a source of sound producing desired acoustic signals at the position of the receiver of sound,
a desired subset of the plurality of acoustic signal data samples includes acoustic data samples of sounds the receiver of sound desires to hear,
the one or more sources of sound include a source of sound 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 machine learning training corpus for training a target machine learning model, the machine learning training corpus comprising at least a sampling of the plurality of acoustic data samples, and the target machine learning model, once trained, is configured to generate an inference indicating a likely target sound from an input mixture of acoustic signals that includes target sounds desired by the receiver and interfering sounds that interfere with the target sounds intended for the receiver, wherein creating the machine learning training corpus includes:
sampling acoustic data samples from one or more sources of sound of the one or more sources of sound that are positioned within a predetermined distance of the position of the receiver of sound to form the desired subset, and
sampling acoustic data samples from one or more sources of sound of the one or more sources of sound that are positioned beyond the predetermined distance of the position of the receiver of sound to form the interfering subset; and

wherein the target machine learning model, once trained using the machine learning training corpus, comprising a proximity-based enhancement machine learning model that enables an enhancement of nearby speech while suppressing far-away speech.