Audio Classification Based On Wavelet And Hidden Markov Mdoels

Audio information processing plays an important role in multimedia applications. Raw audio data is non-semantic and non-structured binary stream, how to extract the structure information and semantic content from raw audio is crucial to deeper processing of audio information, content-based audio retrieval and video parsing with audio assistance. As the core technology of audio structuring, content-based audio classification is a current studied hotspot of audio content automatic analysis.This paper focuses on the two key points of audio classification: feature analysis and extraction, classification algorithm. Main aspects of the paper as follows:We introduce the basic theory and algorithms of Hidden Markov models briefly. Discriminating features between speech and music are deeply analyzed and calculated, which are extracted at frame-level and clip-level. A classifier based on EMGD_HMM (Ergodic Mixed Gaussian Density HMM) is proposed to classify speech, music, and their mixed audio. The classifier uses ergodic Markov chains, which can better describe the variety characteristic of audio states. Experimental comparisons show that the classifier has a high accuracy for audio classification. A method that collaborates wavelet analysis and Fourier analysis is used to extract audio features. Sub-band energy and pitch are extracted by wavelet, the others by Fourier. EMGD_HMM is used to evaluated the performance of feature set, experimental results show that the method is efficient for audio feature extraction.