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The Automatic Repairing Method Addressing Clipping Distortions and Frictional Noises in Electronic Stethoscope

机译:解决电子听诊器削波失真和摩擦噪声的自动修复方法

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The auscultation signal collected by the electronic stethoscope may be sometimes accompanied by various interferences, including external speech/acoustic interferences, clipping distortions, frictional noises, etc. The external speech/acoustic interferences can be eliminated by adaptive filtering, with the aid of an extra recording sensor. However, clipping distortions and frictional noises cannot be addressed by this methodology, and how to automatically repair them has not been fully discussed in the literatures, which affects the signal quality and further the cardiopulmonary sound automatic diagnosis. In this paper, the repairing method that automatically addresses clipping distortions and frictional noises for electronic stethoscope is developed. A simple signal difference method is introduced to automatically detect the clipping distortion regions, and these regions are repaired by the Hermite interpolation. The regions that frictional noises exist are detected by employing Mel-frequency cepstral coefficients (MFCCs) and support vector machine (SVM), and they are repaired by involving the empirical mode decomposition (EMD) as well as correlation coefficients. The proposed method can automatically detect, locate and ultimately repair multiple regions of clipping distortions and frictional noises, and applying it in recorded real auscultation data proves its efficiency.
机译:由电子听诊器收集的听诊信号有时可能伴有各种干扰,包括外部语音/声学干扰,削波失真,摩擦噪声等。可以通过自适应滤波消除外部语音/声学干扰,这需要额外的帮助。记录传感器。然而,这种方法不能解决削波畸变和摩擦噪声,并且如何自动修复削波畸变和摩擦噪声尚未在文献中得到充分讨论,这影响了信号质量,进而影响了心肺声音的自动诊断。本文提出了一种自动解决电子听诊器的削波畸变和摩擦噪声的修复方法。引入了一种简单的信号差方法来自动检测削波失真区域,并通过Hermite插值对这些区域进行修复。利用梅尔频率倒谱系数(MFCC)和支持向量机(SVM)来检测存在摩擦噪声的区域,并通过涉及经验模态分解(EMD)和相关系数对其进行修复。所提出的方法可以自动检测,定位并最终修复削波畸变和摩擦噪声的多个区域,并将其应用于记录的真实听诊数据中证明了其效率。

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