The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Nov. 22, 2022

Filed:

Nov. 20, 2020
Applicant:

Apple Inc., Cupertino, CA (US);

Inventors:

Mehrez Souden, Los Angeles, CA (US);

Symeon Delikaris Manias, Cupertino, CA (US);

Joshua D. Atkins, Los Angeles, CA (US);

Ante Jukic, Los Angeles, CA (US);

Ramin Pishehvar, Cupertino, CA (US);

Assignee:

Apple Inc., Cupertino, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G10L 21/0232 (2013.01); H04R 1/40 (2006.01); G10L 25/30 (2013.01); G06N 3/08 (2006.01); H04R 3/00 (2006.01); G10L 21/0216 (2013.01);
U.S. Cl.
CPC ...
G10L 21/0232 (2013.01); G06N 3/08 (2013.01); G10L 25/30 (2013.01); H04R 1/406 (2013.01); H04R 3/005 (2013.01); G10L 2021/02166 (2013.01);
Abstract

A device for processing audio signals in a time-domain includes a processor configured to receive multiple audio signals corresponding to respective microphones of at least two or more microphones of the device, at least one of the multiple audio signals comprising speech of a user of the device. The processor is configured to provide the multiple audio signals to a machine learning model, the machine learning model having been trained based at least in part on an expected position of the user of the device and expected positions of the respective microphones on the device. The processor is configured to provide an audio signal that is enhanced with respect to the speech of the user relative to the multiple audio signals, wherein the audio signal is a waveform output from the machine learning model.


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