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. 07, 2022

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:
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 respective segments of an input acoustic time series for a same scene. Using a nearest neighbor search, respective segments of the input acoustic time series are classified based on the intermediate audio features to generate a final intermediate feature as a classification for the input acoustic time series. Each respective 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 time series, dividing the same scene into the different windows having varying MFCC features, and feeding the MFCC features of each window 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 windows.


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