US 2026/0038475 A1 · MANIFOLD LEARNING FOR SOUND FIELD ESTIMATION

Abstract

System and methods are provided for estimating the sound field from partial observations. Estimating an acoustic environment for virtual reality and augmented reality applications is a step in the creation of simulated acoustic sound scenes. In particular, the impulse responses of room can be estimated with a generative model. In a teleconferencing scenario with remote participants and a group of participants in a common physical space, giving the remote participants the impression that all other participants are sitting is in the same room acoustically requires filtering the speech of the remote participants with impulse responses estimated at the desired rendering position in the conference room.

Claims20

Dependent claims

A computer-implemented method comprising:

receiving, for a first position associated with a near end room, room data comprising input audio data and target audio data;
determining input data from the first position, the input audio data, and a second position associated with the near end room;
applying initial filter parameters to the input data that results in filtered data;
determining an estimated loss from the filtered data and the target audio data;
determining a matrix from a decoder of a trained generative model;
combining the matrix, the estimated loss, and a step value that results in a tangent vector;
determining updated filter parameters from the decoder and the tangent vector;
receiving far end audio data;
and
determining near end audio data from the far end audio data, the updated filter parameters, and the second position.

7 more elements

One or more non-transitory computer-readable storage media storing computer executable instructions that when executed by a computing system perform operations comprising:

receiving, for a first position associated with a near end room, room data comprising input audio data and target audio data;
determining input data from the first position, the input audio data, and a second position associated with the near end room;
applying initial filter parameters to the input data that results in filtered data;
determining an estimated loss from the filtered data and the target audio data;
determining a matrix from a decoder of a trained generative model;
combining the matrix, the estimated loss, and a step value that results in a tangent vector;
determining updated filter parameters from the decoder and the tangent vector;
receiving far end audio data;
and
determining near end audio data from the far end audio data, the updated filter parameters, and the second position.

7 more elements

Dependencies are inferred from the claim text.

Manifold learning for sound field estimation

Life of this patent

Manifold learning for sound field estimation

Life of this patent

Abstract

Claims20