基于注意的卷积双向门控循环单元的鼾声检测方法

IF 0.2 Q4 ACOUSTICS
Min-soo Kim, Gi Yong Lee, Hyoung‐Gook Kim
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引用次数: 0

摘要

打鼾声是睡眠呼吸暂停患者的重要症状之一,本文提出了一种自动检测打鼾声的方法。在该方法中,输入睡眠过程中产生的声音信号来检测声音产生部分,并将检测到的声音部分变换后的频谱图应用到基于具有注意机制的卷积双向门控循环单元(CBGRU)的分类器中。所应用的注意机制通过扩展CBGRU模型学习判别特征表示来提高打鼾声音检测的性能。实验结果表明,本文提出的鼾声检测方法比现有方法准确率提高了3.1% ~ 5.5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Snoring sound detection method using attention-based convolutional bidirectional gated recurrent unit
This paper proposes an automatic method for detecting snore sound, one of the important symptoms of sleep apnea patients. In the proposed method, sound signals generated during sleep are input to detect a sound generation section, and a spectrogram transformed from the detected sound section is applied to a classifier based on a convolutional bidirectional gated recurrent unit (CBGRU) with attention mechanism. The applied attention mechanism improved the snoring sound detection performance by extending the CBGRU model to learn discriminative feature representation for the snoring detection. The experimental results show that the proposed snoring detection method improves the accuracy by approximately 3.1 % ~ 5.5 % than existing method.
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来源期刊
CiteScore
0.60
自引率
50.00%
发文量
1
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