Typical abnormal sound alarm method for elevator based on depth single classifier

A technology of abnormal sound and classifier, applied in alarms, instruments, voice analysis, etc., can solve problems such as casualties, reduce labor costs, and save lives and property losses.

Active Publication Date: 2019-10-22
HANGZHOU DIANZI UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

If you are lucky, you will be rescued in time, and if no one finds the trapped e

Method used

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  • Typical abnormal sound alarm method for elevator based on depth single classifier
  • Typical abnormal sound alarm method for elevator based on depth single classifier
  • Typical abnormal sound alarm method for elevator based on depth single classifier

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Embodiment Construction

[0061] The present invention will be further described below in conjunction with accompanying drawing and example.

[0062] Such as figure 1 As shown, for the five typical abnormal sounds that may be produced by elevator trapped people, collect preprocessing and perform feature extraction and model training respectively to obtain their respective abnormal sound models.

[0063] Such as figure 2 As shown, the process of extracting abnormal sound features of elevators for MFCC is as follows. The present invention establishes a sound sample library as a target class by collecting abnormal sound signals generated by elevator personnel being trapped. First, normalization, pre-emphasis and frame-based A series of sound processing methods such as windows preprocess the target original sound signal, and then fast Fourier transform the preprocessed sound signal into the frequency domain, then take the square of the absolute value to obtain the energy of each frame signal, and convert...

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Abstract

The invention discloses a typical abnormal sound alarm method for an elevator based on a depth single classifier. The method comprises the following steps of step 1, respectively extracting RMS features of five typical abnormal sound signals; step 2, respectively extracting spectral centroid features of the five typical abnormal sound signals; step 3, respectively extracting MFCC features of the five typical abnormal sound signals; step 4, performing feature extraction on input training samples; step 5, computing an output weight beta of classified learning; step 6, computing a single classifier threshold value theta; and step 7, inputting test data for testing. According to the method, the reaction of a trapped person in the elevator can be rapidly and accurately detected and identified,and through linkage with an automatic alarm system, rapid alarm is facilitated, and the personnel can be notified in time. A guarantee is provided for safe and efficient operation of the elevator, thetimely treatment of the danger and the reduction of the loss of life and property.

Description

technical field [0001] The invention belongs to the field of digital signal processing of intelligent elevators, and relates to a method for alarming typical abnormal sounds of elevators based on a deep single classifier. Background technique [0002] With the progress of urbanization, elevators are widely used as a popular vertical means of transportation. However, elevator failures cause people to be trapped and accidents happen from time to time. Once an elevator fails or people are trapped, how to communicate the danger in a timely and effective manner, and carry out rescue and disposal at the first time to save the loss of life and property has become an important issue for government supervision departments, elevator manufacturers, etc. Businesses, elevator suppliers and other departments urgently need to solve the top priority. [0003] During the safe operation of the elevator, there is theoretically no abnormal sound in the elevator car; once a passenger is trapped...

Claims

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Application Information

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IPC IPC(8): G10L15/02G10L15/06G10L25/18G10L25/24G10L25/51G08B21/02G06K9/62
CPCG10L15/02G10L15/063G10L25/18G10L25/24G10L25/51G08B21/0225G06F18/24G06F18/214
Inventor 曹九稳戴浩桢张俊锋
Owner HANGZHOU DIANZI UNIV
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