Study on audio classification based on 1-state HMM
基于单状态HMM的音频分类方法研究

Hidden markov model (HMM),based on statistical signal, plays an important role in content-based audio retrieval system. According to the characteristic that pays more attention to the type than to content of audio classification, 1-state HMM was used for audio classification, which overcame the shortcoming of assumption of multi-state HMM model's initial state probabilities and state transition probabilities in the course of model-initializing. The experiment shows the method for audio classification based on 1-state HMM could decrease the misrecognition effectively and increase the accuracy of audio classification.