Sound detection model training method and device, and sound event detection method

A sound detection and training method technology, applied in neural learning methods, biological neural network models, computer components, etc., can solve problems such as hearing loss and low efficiency, and achieve the effect of lowering the detection threshold and improving detection efficiency

Pending Publication Date: 2022-03-29
MIDEA GRP CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the method of artificial monitoring is inefficient, and it is easy to cause damage to people's hearing.

Method used

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  • Sound detection model training method and device, and sound event detection method
  • Sound detection model training method and device, and sound event detection method
  • Sound detection model training method and device, and sound event detection method

Examples

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Embodiment 1

[0104] figure 1 One of the flowcharts showing the training method of the sound detection model according to the embodiment of the present invention, specifically, the training method of the sound detection model may include the following steps:

[0105]Step 102, obtaining a pre-stored training sound signal, and performing feature extraction on it, thereby establishing a two-dimensional feature map training set;

[0106] In step 104, the preset neural network model is imported into the two-dimensional feature map training set, and the neural network model is trained through a loss function based on the hidden Markov model, thereby obtaining a sound detection model.

[0107] In the embodiment of the present invention, by training a neural network model capable of automatically realizing sound event detection, that is, a sound detection model, it is beneficial to realize automatic sound event detection and improve the efficiency of sound event detection.

[0108] Specifically, f...

Embodiment 2

[0113] figure 2 The second flow chart showing the training method of the sound detection model according to the embodiment of the present invention, specifically, the training method of the sound detection model may include the following steps:

[0114] Step 202, divide the training sound signal into frames to obtain sample frames;

[0115] Step 204 , performing windowing processing on the sample frames by using a preset first window function, and performing feature extraction on the windowed sample frames to obtain a two-dimensional feature map training set.

[0116] In the embodiment of the present invention, when performing feature extraction on sound, the sound signal is first subjected to frame processing to obtain sample frames of a certain length, wherein each sample frame is a sound segment, and the interval between two adjacent sample frames is There may be some overlap between them. Wherein, the frame length (the duration of the sound sample) and the overlapping l...

Embodiment 3

[0120] image 3 The third flow chart showing the training method of the sound detection model according to the embodiment of the present invention, specifically, the training method of the sound detection model may include the following steps:

[0121] Step 302, input the posterior probability that the sample frame is an event frame into the target loss function;

[0122] Step 304, obtaining the current loss value output by the target loss function, and continuously training the preset neural network model with the target loss value range as the target until the current loss value falls within the target loss value range.

[0123] In the embodiment of the present invention, when training the preset neural network model through the two-dimensional feature map training set, the two-dimensional feature map training set is input into the neural network model, and the neural network model will The feature map training set outputs a specific result, specifically the posterior proba...

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Abstract

The invention provides a training method and device of a sound detection model and a detection method of a sound event, and the training method of the sound detection model comprises the steps: obtaining a training sound signal, carrying out the feature extraction of the training sound signal, and building a two-dimensional feature map training set; and importing the neural network model into the two-dimensional feature map training set, and training the neural network model through a loss function based on a hidden Markov model to obtain a sound detection model. According to the embodiment of the invention, through the loss function defined based on the hidden Markov model, an occurring specific event can be identified through sound event detection, timely response can be made for the occurring event, personnel intervention is not needed, sound detection does not depend on experienced workers any more, on one hand, the detection efficiency is improved, and on the other hand, the detection time is shortened. On the other hand, the detection threshold is reduced, and the hearing of people is not damaged.

Description

technical field [0001] The present invention relates to the technical field of sound detection, in particular, to a training method for a sound detection model, a training device for a sound detection model, a detection method for a sound event, a detection device for a sound event, and a computer device, a computer-readable storage medium, and an electronic device. Background technique [0002] In related technologies, sound quality inspection of products is an important link in factory production. You can manually monitor the sound to judge whether there are quality problems such as the screw is not tightened, the heating tube is not stuck in the bracket, etc. However, the method of artificial monitoring is inefficient, and it is easy to cause damage to people's hearing. Contents of the invention [0003] The present invention aims to solve at least one of the technical problems existing in the prior art or related art. [0004] To this end, the first aspect of the pr...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/00G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F2218/08G06F18/2415G06F18/214
Inventor 冯祺徽曹海涛
Owner MIDEA GRP CO LTD
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