基于单状态HMM的音频分类方法研究

经典的隐马尔可夫模型(HMM)是一种基于统计信号的模型，它在基于内容的音频检索系统中具有重要的作用。根据音频分类重类型轻内容的特性，将单状态的HMM用于音频分类，克服了多状态HMM在模型初始化时状态初始概率和转移概率赋值带有假设不准确的缺点。实验结果表明基于单状态的HMM模型音频分类方法能有效地减少误识率，提高音频分类的精确度。

Study on audio classification based on 1-state HMM

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.