Noise data artificial intelligence apparatus and pre-conditioning method for identifying source of problematic noise

Patent metadata

Field	Value
Publication number	US-2020193291-A1
Application number	US-201916687499-A
Country	US
Kind code	A1
Filing date	Nov 18, 2019
Priority date	Dec 13, 2018
Publication date	Jun 18, 2020
Grant date	—

Abstract

A noise data artificial intelligence learning method for identifying the source of problematic noise may include a noise data pre-conditioning method for identifying the source of problematic noise including: selecting a unit frame for the problematic noise among noises sampled with time; dividing the unit frame into N segments; analyzing frequency characteristic for each segment of the N segments and extracting a frequency component of each segment by applying Log Mel Filter; and outputting a feature parameter as one representative frame by averaging information on the N segments, wherein an artificial intelligence learning by the feature parameter extracted according to a change in time by the noise data pre-conditioning method applies Bidirectional RNN.

First claim

What is claimed is:

1 . A noise data artificial intelligence learning method of identifying a source of problematic noise, the method comprising:
a noise data pre-conditioning method for identifying the source of the problematic noise, including: selecting a unit frame for the problematic noise among noises sampled with time; dividing the unit frame into N segments; analyzing frequency characteristic for each segment of the N segments and extracting a frequency component of each segment by applying Log Mel Filter; and outputting a feature parameter as one representative frame by averaging information on the N segments, wherein an artificial intelligence learning by the feature parameter extracted according to a change in time by the noise data pre-conditioning method applies Bidirectional RNN.

2 . The noise data artificial intelligence learning method of identifying the source of the problematic noise of claim 1 ,
wherein the sampling is configured to sample in a range of twice a problematic frequency band.

3 . The noise data artificial intelligence learning method of identifying the source of the problematic noise of claim 1 ,
wherein an overlap is set between the unit frame with time and a next unit frame.

4 . The noise data artificial intelligence learning method of identifying the source of the problematic noise of claim 1 ,
wherein the artificial intelligence learning additionally is configured to apply Deep Neural Network (DNN).

5 . The noise data artificial intelligence learning method of identifying the source of the problematic noise of claim 4 ,
wherein the artificial intelligence learning is configured to additionally apply Attention Mechanism.

6 . The noise data artificial intelligence learning method of identifying the source of the problematic noise of claim 5 ,
wherein the artificial intelligence learning is configured to additionally apply an Early stage ensemble algorithm.

7 . The noise data artificial intelligence learning method of identifying the source of the problematic noise of claim 6 ,
wherein when a time axis of problematic noise learning data is constantly collected, an Ensemble model of jointly trained RNNs algorithm using a time-frequency map and an engine RPM-frequency map for improving the accuracy is additionally applied.

8 . A noise data artificial intelligence diagnostic apparatus implemented with the noise data artificial intelligence learning method of identifying the source of the problematic noise of claim 7 ,
wherein a noise of a vehicle or a powertrain is directly measured by an input device of the apparatus, or stored noise data is provided through a storage medium.

Assignees

• Hyundai Motor Co Ltd
• Kia Motors Corp
• Iucf Hyu Industry Univ Corporation Foundation Hanyang Univ

Inventors

• Lee Dong-Chul
• Jung In-Soo
• Chang Joon-Hyuk
• Noh Kyoung-Jin

Classifications

• G06F18/214

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• G06N3/045 Primary

  Combinations of networks · CPC title

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title