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:
Feb. 23, 2021

Filed:

Aug. 19, 2020
Applicant:

Nec Laboratories America, Inc., Princeton, NJ (US);

Inventors:

Cristian Lumezanu, Princeton Junction, NJ (US);

Yuncong Chen, Plainsboro, NJ (US);

Dongjin Song, Princeton, NJ (US);

Takehiko Mizuguchi, Princeton, NJ (US);

Haifeng Chen, West Windsor, NJ (US);

Bo Dong, Richardson, TX (US);

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G10L 25/51 (2013.01); G10L 25/24 (2013.01); G10L 25/18 (2013.01); G10L 25/21 (2013.01);
U.S. Cl.
CPC ...
G10L 25/51 (2013.01); G10L 25/24 (2013.01); G10L 25/18 (2013.01); G10L 25/21 (2013.01);
Abstract

A method is provided. Intermediate audio features are generated from an input acoustic sequence. Using a nearest neighbor search, segments of the input acoustic sequence are classified based on the intermediate audio features to generate a final intermediate feature as a classification for the input acoustic sequence. Each segment corresponds to a respective different acoustic window. The generating step includes learning the intermediate audio features from Multi-Frequency Cepstral Component (MFCC) features extracted from the input acoustic sequence. The generating step includes dividing the same scene into the different acoustic windows having varying MFCC features. The generating step includes feeding the MFCC features of each of the different acoustic windows into respective LSTM units such that a hidden state of each respective LSTM unit is passed through an attention layer to identify feature correlations between hidden states at different time steps corresponding to different ones of the different acoustic windows.


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