Characteristic Analysis and Test of Mixed Pulley Sound Signal Based on Hidden Markov Model

2020 IEEE International Conference on Smart Internet of Things (SmartIoT) Pub Date : 2020-08-01 DOI:10.1109/SmartIoT49966.2020.00043

Gengzhe Zheng, Liming Wu, Feiyang Song, Xinying He, Gengxuan Lin, Danfeng Jiang

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Abstract

The sliding door pulley produces noise in the process of movement and forms mixed noise after combining with the environmental background noise. The noise produced by pulley movement is related to its structure and material. This article discusses how to use sound signals to analyze pulley characteristics. Noise source is separated by simulating the mixing of a given sound source through the Clustering Hidden Markov Model (C-HMM), and the mechanism of the noise source generated by the pulley is studied, which can provide data support for judging the pulley mass. This paper designs a mixed pulley sound signal perception test platform based on C-HMM. The experimental results show that the system can analyze and judge the quality of the pulley under certain conditions by entering the noise characteristics of sheave motion. The characteristic recognition rate of different combinations is different, and the comprehensive recognition accuracy of the Mel Frequency Cepstrum Coefficient (MFCC) + Short Time Energy (STE) combination can reach 91.76%.

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基于隐马尔可夫模型的混合滑轮声信号特性分析与测试

滑动门滑轮在运动过程中产生噪声，与环境背景噪声结合后形成混合噪声。滑轮运动产生的噪声与其结构和材料有关。本文讨论了如何利用声音信号分析滑轮的特性。通过聚类隐马尔可夫模型(C-HMM)模拟给定声源的混合，分离噪声源，研究滑轮产生噪声源的机理，为判断滑轮质量提供数据支持。本文设计了一个基于C-HMM的混合滑轮声信号感知测试平台。实验结果表明，该系统通过输入滑轮运动的噪声特性，可以在一定条件下对滑轮的质量进行分析和判断。不同组合的特征识别率不同，Mel Frequency倒频谱系数(MFCC) + Short Time Energy (STE)组合的综合识别准确率可达91.76%。

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2020 IEEE International Conference on Smart Internet of Things (SmartIoT)

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