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首页> 外文期刊>Annals of Biomedical Engineering: The Journal of the Biomedical Engineering Society >A Bag of Wavelet Features for Snore Sound Classification
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A Bag of Wavelet Features for Snore Sound Classification

机译：一袋小波特征，用于打鼾声音分类

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摘要

Snore sound (SnS) classification can support a targeted surgical approach to sleep related breathing disorders. Using machine listening methods, we aim to find the location of obstruction and vibration within a subject's upper airway. Wavelet features have been demonstrated to be efficient in the recognition of SnSs in previous studies. In this work, we use a bag-of-audio-words approach to enhance the low-level wavelet features extracted from SnS data. A Naive Bayes model was selected as the classifier based on its superiority in initial experiments. We use SnS data collected from 219 independent subjects under drug-induced sleep endoscopy performed at three medical centres. The unweighted average recall achieved by our proposed method is 69.4%, which significantly (p<0.005, one-tailed z-test) outperforms the official baseline (58.5%), and beats the winner (64.2%) of the INTERSPEECH ComParE Challenge 2017 Snoring sub-challenge. In addition, the conventionally used features like formants, mel-scale frequency cepstral coefficients, subband energy ratios, spectral frequency features, and the features extracted by the openSMILE toolkit are compared with our proposed feature set. The experimental results demonstrate the effectiveness of the proposed method in SnS classification.
机译：Snore Sound（SNS）分类可以支持有针对性的手术方法来睡眠相关的呼吸障碍。使用机器聆听方法，我们的目标是在受试者的上呼吸道内找到阻塞和振动的位置。已经证明小波特征在以前的研究中识别SNSS的效率是有效的。在这项工作中，我们使用一个禁止音频字的方法来增强从SNS数据中提取的低级小波功能。基于初始实验中的优势，选择了一个天真的贝叶斯模型作为分类器。我们使用在药物诱导的睡眠内窥镜下从219个独立对象收集的SNS数据进行三个医疗中心。我们提出的方法实现的未加权平均召回是69.4％，其显着（P <0.005，单尾Z-Test）优于官方基线（58.5％），并击败了2017年的赢家（64.2％）的Interspeech比较挑战打鼾子挑战。此外，与我们所提出的功能集相比，将常规使用的特征如素质，梅尔级频率谱系统，子带能量比，谱频率，以及由开放式工具包提取的功能。实验结果表明了所提出的方法在SNS分类中的有效性。

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