DHMM在机械设备音频识别中的应用  

Application of DHMM to mechanical equipment audio recognition

作　　者：苏鹏[1] 程健[1] 

机构地区：[1]中国科学技术大学自动化系,合肥230027

出　　处：《计算机工程与应用》2015年第1期266-270,共5页Computer Engineering and Applications

摘　　要：为了对现场机械或设备进行监控、诊断和识别,以音频为监控手段,引入矢量量化(VQ)算法并建立机械设备音频的离散隐Markov模型(DHMM)。特征参数采用MFCC,码书设计采用Linde-Buzo-Gray(LBG)算法;推导出Baum-Welch算法参数重估的多观察序列的最简标定形式;分析了多种HMM类型,提出了适合机械设备音频的HMM。实验在22种音频中进行,识别准确率在97%以上,证明了方法的有效性。In order to monitor, diagnose and identify the machinery or equipment, the audio signal is used as the monitoring means, and the Vector Quantization （VQ） algorithm is introduced, also mechanical equipment＇s Discrete Hidden Markov Model（DHMM） is established. The mechanical equipment audio parameter extracted is MFCC, and the code book is pro- duced using Linde-Buzo-Gray（LBG） algorithm. The Baum-Welch algorithm＇s simplest scaling factor form based on mul- tiple sequences is deduced. Meanwhile logarithmic form of Viterbi algorithm is used. Various forms of HMM model are compared through the experiments, and the suitable audio HMM model form for mechanical equipment is chosen. The experiments on 22 kinds of audio signals, with the recognition accuracy rate of more than 97%, prove the validity of the method.

关 键 词：Mel倒谱系数(MFCC) 矢量量化 LBG算法 隐马尔科夫模型 音频识别 设备监控 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]