Supervised snore source identifying method

Abstract

The invention discloses a supervised snore source identifying method. The method includes data pretreatment, training and identification; the main steps include, firstly, performing Mel frequency conversion on actual measured snore data, and acquiring a data sample; secondly, arranging a structure of a convolution neutral network, quantity of output feature drawings of a convolution layer and convolution core size, pooling size, weight vector updating studying rate, batch training sample number, and training iterations; thirdly, using a snore time frequency spectrogram of a training set as the convolution neutral network to input; performing network initialization on the arranged network structure; completing the training process through forward process, direction bias transmission, weight value updating and offset until the appointed iterations; at last, delivering the testing set to the trained network model and acquiring the identifying result. The supervised snore source identifying method can effectively identify the snore source, and is exact in identifying result and good in performance.

Description

technical field [0001] The invention belongs to the technical field of non-speech recognition, in particular to a supervised snoring sound source recognition method. Background technique [0002] Obstructive sleep apnea / hypopnea syndrome is a sleep-breathing disorder closely related to upper airway obstruction, structural airway narrowing, and lowered upper airway muscle tone. Obstructive sleep apnea syndrome alone affects approximately 15 million adults in the United States, and is common in patients with high blood pressure and other cardiovascular conditions, including coronary heart disease, stroke and atrial fibrillation. The incidence rate of obstructive sleep apnea / hypopnea syndrome is high worldwide, and the incidence rate of men is higher than that of women (the prevalence rate of adult men is 3% to 7%, and that of adult women is 2% to 5%). Obstructive sleep apnea / hypopnea syndrome is often accompanied by symptoms such as snoring, sleep structure disturbance, frequ...

AI Technical Summary

Problems solved by technology

However, the multi-layer neural network has a complex structure, too many parameters need to be trained, and the efficiency of learning features is low.

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