Pattern recognition approach to identify loose particle material based on modified MFCC and HMMs

This paper presented a revised particle impact noise detection (PIND) system to detect loose particles in the sealed electronic devices. The type of particle material is the valuable information to trace the particle' origins and assess the potential harmfulness. The most traditional classification model cannot effectively recognize the types of material due to particle impact acoustic signals with characteristics such as non-stationary, poor repeatability and reproducibility. To address this problem, a new method of pattern recognition for material identification is proposed based on modified Mel frequency cepstral coefficient (MFCC) and hidden Markov model (HMM). First, to be more suitable for target identification of different material of particle acoustic signals, the relationship between the MFCC features and the Mel filter bank is derived, and the modified filter bank is proposed. The modified MFCC features are then mapped into HMMs, where the model number with the maximum probability is chosen as the identified class. Finally, experiments have been carried out with different features and classifiers. The results show that the proposed method can effectively distinguish the four types of materials including wire, chip, aluminum and tin.