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

Abstract: 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 produced using Linde-Buzo-Gray（LBG） algorithm. The Baum-Welch algorithm’s simplest scaling factor form based on multiple 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.

Key words: Mel Frequency Cepstrum Coefficient（MFCC）, Vector Quantization（VQ）, Linde-Buzo-Gray（LBG） algorithm, Discrete Hidden Markov Model（DHMM）, audio recognition, equipment monitoring