Abnormal sound detection model training method and device and computer storage medium

Abstract

The invention discloses an abnormal sound detection model training method and device and a computer storage medium, and the method comprises the following steps: intercepting a sound segment with a preset duration into N sub-segments, carrying out the sampling and filtering of each sub-segment by employing H band-pass filters with different frequency bands, and forming N*H*W three-dimensional feature tensors; inputting the plurality of three-dimensional feature tensors into a three-dimensional convolutional neural network for training, wherein the plurality of three-dimensional feature tensorscorrespond to a plurality of sound segments with preset durations; the plurality of sound segments with the preset duration comprise a positive sample with abnormal sound and a negative sample without abnormal sound; and calculating loss by adopting a loss function for simultaneously evaluating the positive sample and the negative sample, and updating parameters of an abnormal sound detection model. The problems of inaccurate identification and low operation efficiency in the existing sound anomaly detection are solved.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a training method, device and computer storage medium for an abnormal sound detection model. Background technique [0002] In the application of sound anomaly detection, the most cutting-edge method in the prior art is to train the classification network based on circular convolution through label samples, which has the following disadvantages: [0003] 1. In terms of feature extraction, existing methods mostly use hand-designed features (such as Mel cepstral coefficients (MFCC)). These methods are too dependent on the perception level of the problem and are not intelligent enough, and cannot guarantee the most suitable for the current application scenario. Excellent. [0004] 2. In terms of recognition models, existing methods mostly use circular convolutional networks and their variants as the backbone network. These models rely on the previous intermediate results in the re...

AI Technical Summary

Problems solved by technology

[0007] The main purpose of the present invention is to provide a training method, device and computer storage medium for an abnormal sound detection model, aiming to solve the problems of inaccurate recognition and low operating efficiency in the existing abnormal sound detection

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