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:
Apr. 19, 2022

Filed:

Aug. 07, 2020
Applicant:

Samsung Electronics Co., Ltd., Gyeonggi-do, KR;

Inventors:

Soonho Baek, Gyeonggi-do, KR;

Kiho Cho, Gyeonggi-do, KR;

Jongmo Keum, Gyeonggi-do, KR;

Beakkwon Son, Gyeonggi-do, KR;

Myoungho Lee, Gyeonggi-do, KR;

Yonghoon Lee, Gyeonggi-do, KR;

Hangil Moon, Gyeonggi-do, KR;

Jaemo Yang, Gyeonggi-do, KR;

Gunwoo Lee, Gyeonggi-do, KR;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G10L 21/0232 (2013.01); H04R 1/40 (2006.01); H04R 3/00 (2006.01); H04R 29/00 (2006.01); G10L 25/51 (2013.01); G10L 25/30 (2013.01); H04S 3/00 (2006.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); G10L 21/0364 (2013.01); G10L 21/0216 (2013.01);
U.S. Cl.
CPC ...
G10L 21/0232 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G10L 21/0364 (2013.01); G10L 25/30 (2013.01); G10L 25/51 (2013.01); H04R 1/406 (2013.01); H04R 3/005 (2013.01); H04R 29/005 (2013.01); H04S 3/008 (2013.01); G10L 2021/02166 (2013.01); H04S 2400/01 (2013.01); H04S 2400/15 (2013.01);
Abstract

According to an embodiment, an electronic device may include a memory configured to store a noise removal neural network model and data utilized in the noise removal neural network model and a processor electrically connected to the memory wherein the memory may store instructions that, when executed, enable the processor to: output a first channel signal using a first beamformer for a multi-channel audio signal; output a second channel signal using a second beamformer; generate a third channel signal that compensates for a difference in noise levels between the first channel signal and the second channel signal; and train the noise removal neural network model by using the third channel signal in which the difference in noise levels is compensated for and the first channel signal as input values.


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