Mr. Xin Liu Profile

Xin Liu

at Lanzhou Aopu Information Technology Co., Ltd.

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 24 November 2021 Paper

Exploration and application of convolutional neural network to improve the quality of DAS voice print machine recognition and acoustic reduction

Liu Xin, Wenbo Shang, Shuanxiong Ma, Xiaoyan Li, Zhang Yan, Zhaodong Zhang

Proceedings Volume 12061, 120610A (2021) https://doi.org/10.1117/12.2602043

Read Abstract +

In order to solve the technical problems of distributed acoustic sensor(DAS)system in many kinds of acoustic signal recognition, In this paper, an implementation path of acoustic signal machine recognition technology is proposed, which realizes the voice print and acoustic wave restoration of DAS machine recognition, and improves the quality of recognition and restoration.. Methods by preprocessing the optical fiber signal caused by sound, including signal framing, windowing, short-time Fourier transform and signal enhancement, the signal was transformed into a spectrogram, and the convolution neural network (CNN) was used to train the 13400 datas collected from the scene to machine judge the four types of acoustic signals: mechanical excavation, manual operation, vehicle passing and gas leakage Type. According to 800 test datas, the accuracy, recall and F1 value of the model are 96.4%, 96.5% and 96.0% respectively, and the average recognition speed is 30s. The feasibility of CNN model to realize and improve the quality of DAS machine voice print recognition and acoustic reduction was verified by field tests.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years