公开(公告)号：US20230379645A1

公开(公告)日：2023-11-23

申请号：US17748356

申请日：2022-05-19

Applicant: Google LLC

Inventor： Rajeev Conrad Nongpiur ,  Qian Zhang ,  Andrew James Sutter ,  Kung-Wei Liu ,  Jihan Li ,  Hélène Bahu ,  Leonardo Kusumo ,  Sze Chie Lim ,  Marco Tagliasacchi ,  Neil Zeghidour ,  Michael Takezo Chinen

IPC: H04S7/00 ,  G10L19/008 ,  H04R5/027 ,  H04R3/00 ,  H04S3/00 ,  G06N20/00

CPC classification number: H04S7/30 ,  G10L19/008 ,  H04R5/027 ,  H04R3/005 ,  H04S3/008 ,  G06N20/00 ,  H04S2420/11 ,  H04S2400/11 ,  H04S2400/15 ,  H04S2420/03 ,  H04S2400/01 ,  H04R2420/07

Abstract: The technology generally relates to spatial audio communication between devices. For example, a first device and a second device may be connected via a communication link. The first device may capture audio signals in an environment through two or more microphones. The first device may encode the captured audio with spatial configuration data. The first device may transmit the encoded audio via the communication link to the second device. The second device may decode the encoded audio into binaural or ambisonic audio to be output by one or more speakers of the second device. The binaural or ambisonic audio may be converted into spatial audio to be output. The second device may output the binaural or spatial audio to create an immersive listening experience.

公开(公告)号：US20230368804A1

公开(公告)日：2023-11-16

申请号：US18144413

申请日：2023-05-08

Applicant: Google LLC

Inventor： Willem Bastiaan Kleijn ,  Jan K. Skoglund ,  Alejandro Luebs ,  Sze Chie Lim

IPC: G10L19/02 ,  G10L25/30

CPC classification number: G10L19/0204 ,  G10L25/30

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for coding speech using neural networks. One of the methods includes obtaining a bitstream of parametric coder parameters characterizing spoken speech; generating, from the parametric coder parameters, a conditioning sequence; generating a reconstruction of the spoken speech that includes a respective speech sample at each of a plurality of decoder time steps, comprising, at each decoder time step: processing a current reconstruction sequence using an auto-regressive generative neural network, wherein the auto-regressive generative neural network is configured to process the current reconstruction to compute a score distribution over possible speech sample values, and wherein the processing comprises conditioning the auto-regressive generative neural network on at least a portion of the conditioning sequence; and sampling a speech sample from the possible speech sample values.

公开(公告)号：US20210287038A1

公开(公告)日：2021-09-16

申请号：US17250506

申请日：2019-05-16

Applicant: Google LLC

Inventor： Willem Bastiaan Kleijn ,  Sze Chie Lim ,  Michael Chinen ,  Jan Skoglund

IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04

Abstract: Implementations identify a small set of independent, salient features from an input signal. The salient features may be used for conditioning a generative network, making the generative network robust to noise. The salient features may facilitate compression and data transmission. An example method includes receiving an input signal and extracting salient features for the input signal by providing the input signal to an encoder trained to extract salient features. The salient features may be independent and have a sparse distribution. The encoder may be configured to generate almost identical features from two input signals a system designer deems equivalent. The method also includes conditioning a generative network using the salient features. In some implementations, the method may also include extracting a plurality of time sequences from the input signal and extracting the salient features for each time sequence.