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US 2025/0103897 A1Published application
Methods And Systems For Training And Providing A Machine Learning Model For Audio Compensation
AssigneeTreble Technologies
InventorsFinnur Pind, Jesper Pedersen
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Abstract
A computer implemented method for training a machine learning model for audio compensation includes: receiving 3D models of a plurality of rooms, each of the 3D models comprising at least one sound source and at least one acoustic property; receiving a plurality of impulse responses at a listening position in each of the plurality of rooms, training the machine learning model for audio compensation using at least the plurality of impulse responses as input. Methods and systems using the disclosed computer implemented method and its features are also disclosed.
Claims27
A computer implemented method for training a machine learning model for audio compensation, the method comprising:
- receiving 3D models of a plurality of rooms, each of the 3D models comprising at least one position of at least one sound source configured to emit sound in each of the 3D models of the plurality of rooms, and at least one acoustic property of at least one boundary;
- receiving at least one listening position in each of the 3D models of the plurality of rooms;
- receiving a plurality of impulse responses at the at least one listening position in each of the 3D models of the plurality of rooms, wherein the plurality of impulse responses is obtained with a wave-based solver and/or a geometrical acoustic solver, wherein the wave-based solver and/or the geometrical acoustic solver determines the plurality of impulse responses of a wave-based propagation of an impulse emitted at the at least one sound source in the 3D models of the plurality of rooms, and received at the at least one listening position;
- and
- training the machine learning model for audio compensation using at least the plurality of impulse responses as input.
2 more elements
A method of compensating audio signals using a machine learning model for audio compensation in an audio device having at least one microphone, comprising:
- receiving an audio signal at the at least one microphone;
- and
- generating a compensated audio signal from the received audio signal using a machine learning model for audio compensation;
- wherein the machine learning model for audio compensation was trained by
- receiving 3D models of a plurality of rooms, each of the 3D models comprising at least one position of at least one sound source configured to emit sound in each of the 3D models of the plurality of rooms, and at least one acoustic property of at least one boundary;
- receiving at least one listening position in each of the 3D models of the plurality of rooms;
- receiving a plurality of impulse responses at the at least one listening position in each of the 3D models of the plurality of rooms, wherein the plurality of impulse responses is obtained with a wave-based solver and/or a geometrical acoustic solver, wherein the wave-based solver and/or the geometrical acoustic solver determines the plurality of impulse responses of a wave-based propagation of an impulse emitted at the at least one sound source in the 3D models of the plurality of rooms, and received at the at least one listening position;
- and
- training the machine learning model for audio compensation using at least the plurality of impulse responses as input.
6 more elements
An audio device comprising:
- at least one microphone configured to receive an audio signal;
- and
- a processing system configured to apply machine learning based audio compensation to the audio signal to generate a compensated audio signal;
- wherein the machine learning based audio compensation was trained by
- receiving 3D models of a plurality of rooms, each of the 3D models comprising at least one position of at least one sound source configured to emit sound in each of the 3D models of the plurality of rooms, and at least one acoustic property of at least one boundary;
- receiving at least one listening position in each of the 3D models of the plurality of rooms;
- receiving a plurality of impulse responses at the at least one listening position in each of the 3D models of the plurality of rooms, wherein the plurality of impulse responses is obtained with a wave-based solver and/or a geometrical acoustic solver, wherein the wave-based solver and/or the geometrical acoustic solver determines the plurality of impulse responses of a wave-based propagation of an impulse emitted at the at least one sound source in the 3D models of the plurality of rooms, and received at the at least one listening position;
- and
- training the machine learning model for audio compensation using at least the plurality of impulse responses as input.
6 more elements
A system for training a machine learning model for audio compensation, the system comprising:
- a computer system having a processor coupled to a memory, the processor configured to:
- receive 3D models of a plurality of rooms, each of the 3D models including at least one position of at least one sound source configured to emit sound in each of the 3D models of the plurality of rooms, and at least one acoustic property of at least one boundary;
- receive at least one listening position in each of the 3D models of the plurality of rooms;
- receive a plurality of impulse responses at the at least one listening position in each of the 3D models of the plurality of rooms, wherein the plurality of impulse responses are obtained with a wave-based solver and/or a geometrical acoustic solver, wherein the wave-based solver and/or the geometrical acoustic solver determines the plurality of impulse responses of a wave-based propagation of an impulse emitted at the at least one sound source in the 3D models of the plurality of rooms, and received at the at least one listening position;
- and
- training the machine learning model for audio compensation using at least the plurality of impulse responses as input.
3 more elements
Dependencies are inferred from the claim text.