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基于EMGD_HMM的音频自动分类 被引量：3

Automatic Audio Classification Based on EMGD_HMM
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摘要 音频自动分类是解决音频结构化问题和提取音频内容语义的重要手段之一,是当前基于内容的音频检索领域的一个研究热点。在考察音频数据特征的基础上,针对左-右密度隐马尔可夫模型(left-right DHMM)不能很好反映音频中状态反复的缺点,提出了一种基于各态历经混合高斯密度隐马尔可夫模型(EMGD_HMM)的分类器,并应用于语音、音乐和它们的混合声音的分类。实验结果表明,EMGD_HMM的分类精度要优于left-right DHMM。 Automatic audio classification is one of the significant methods to extract content semantics from audio. An improved classifier based on EMGD_HMM（Ergodic Mixed Gaussian Density Hidden Markov Model） is proposed to classify audio in speech, music, and their mixture. The experimental results show that compared with left-right DHMM（left-right Density Hidden Markov Model）, EMGD HMM achieves better classification accuracy.
作者 王超 吴亚锋
机构地区 西北工业大学数据处理中心
出处 《电声技术》 2007年第11期52-54,60,共4页 Audio Engineering
关键词 音频自动分类 left-right DHMM模型 EMGD_HMM模型 MEL倒谱系数 automatic audio classification left-right DHMM EMGD_HMM Mel frequency cepstrnm coefficient
分类号 TN912 [电子电信—通信与信息系统]
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参考文献5

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共引文献46

同被引文献11

引证文献3

二级引证文献6

电声技术
2007年 第11期

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