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:
Jun. 30, 2026

Filed:

Feb. 21, 2023
Applicant:

Microsoft Technology Licensing, Llc, Redmond, WA (US);

Inventors:

Sefik Emre Eskimez, Bellevue, WA (US);

Takuya Yoshioka, Bellevue, WA (US);

Huaming Wang, Clyde Hill, WA (US);

Alex Chenzhi Ju, Seattle, WA (US);

Min Tang, Redmond, WA (US);

Tanel Pärnamaa, Tallinn, EE;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G10L 21/0232 (2013.01); G06N 3/0442 (2023.01); G10L 17/02 (2013.01); G10L 17/04 (2013.01); G10L 17/06 (2013.01); G10L 17/18 (2013.01); G10L 21/0208 (2013.01);
U.S. Cl.
CPC ...
G10L 21/0232 (2013.01); G06N 3/0442 (2023.01); G10L 17/02 (2013.01); G10L 17/04 (2013.01); G10L 17/06 (2013.01); G10L 17/18 (2013.01); G10L 2021/02082 (2013.01);
Abstract

A data processing system implements receiving a far-end signal associated with a first computing device participating in an online communication session and receiving a near-end signal associated with a second computing device participating in the online communication session. The near-end signal includes speech of a target speaker, a first interfering speaker, and an echo signal. The system further implements providing the far-end signal, the near-end signal, and an indication of the target speaker as an input to a machine learning model. The machine learning model trained to analyze the far-end signal and the near-end signal to perform personalized noise suppression (PNS) to remove speech from one or more interfering speakers and acoustic echo cancellation (AEC) to remove echoes. The model is trained to output an audio signal comprising speech of the target speaker. The system obtains the audio signal comprising the speech of the target speaker from the model.


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