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:
May. 03, 2022

Filed:

Sep. 12, 2017
Applicant:

Nippon Telegraph and Telephone Corporation, Chiyoda-ku, JP;

Inventors:

Yuma Koizumi, Musashino, JP;

Shoichiro Saito, Musashino, JP;

Kazunori Kobayashi, Musashino, JP;

Hitoshi Ohmuro, Musashino, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G10L 21/0216 (2013.01); G10L 21/0232 (2013.01); G10L 21/0264 (2013.01); G10L 21/0208 (2013.01);
U.S. Cl.
CPC ...
G10L 21/0232 (2013.01); G10L 21/0264 (2013.01); G10L 2021/02082 (2013.01); G10L 2021/02165 (2013.01);
Abstract

A noise estimation parameter learning device is provided according to which even in a large space causing a problem of the reverberation and the time frame difference, multiple microphones disposed at distant positions cooperate with each other, and a spectral subtraction method is executed, thereby allowing the target sound to be enhanced. A noise estimation parameter learning device for learning noise estimation parameters used to estimate noise included in observed signals through a plurality of microphones, the noise estimation parameter learning device comprising: a modeling part that models a probability distribution of observed signals of the predetermined microphone, models a probability distribution of time frame differences, and models a probability distribution of transfer function gains; a likelihood function setting part that sets a likelihood function pertaining to the time frame difference, and a likelihood function pertaining to the transfer function gain, based on the modeled probability distributions; and a parameter update part that alternately and repetitively updates two variables of two likelihood functions, and outputs the time frame difference and the transfer function gain that have converged, as the noise estimation parameters.


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