Methods and systems for training and providing a machine learning model for audio compensation
AssigneeTreble Technologies
InventorsFinnur Pind, Jesper Pedersen
Life of this patent
Estimating the term…
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.
Claims28
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 directive sound source configured to emit sound in a defined direction 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, wherein the wave-based solver determines the plurality of impulse responses of a wave-based propagation of an impulse emitted at the at least one directive 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 using at least the plurality of impulse responses as input, to perform one or more of blind room acoustic modeling, dereverberation, echo-cancellation, speech recognition, beamforming, and virtual sound rendering.
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 directive sound source configured to emit sound in a defined direction 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, wherein the wave-based solver determines the plurality of impulse responses of a wave-based propagation of an impulse emitted at the at least one directive 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, wherein the audio compensation compensates for reverberations or echo.
Dependencies are inferred from the claim text.