采用潜在概率语义模型和K近邻分类器的音频分类算法 Audio Classification Algorithm Using Probabilistic Latent Semantic Models and K Nearest Neighbor Classifier期刊界 All Journals 搜尽天下杂志传播学术成果专业期刊搜索期刊信息化学术搜索

采用潜在概率语义模型和K近邻分类器的音频分类算法

引用本文：	辛欣^1,3,陈曙东^2,3,仝明磊⁴,胡文皓^1,3,刘陈伟^1,3,葛浩栋³. 采用潜在概率语义模型和K近邻分类器的音频分类算法[J]. 华侨大学学报(自然科学版), 2016, 0(2): 196-200. DOI: 10.11830/ISSN.1000-5013.2016.02.0196

作者姓名：	辛欣¹ 3 陈曙东² 3 仝明磊⁴ 胡文皓¹ 3 刘陈伟¹ 3 葛浩栋³

作者单位：	1. 中国科学院大学电子电气与通信工程学院, 北京 100049;2. 中国科学院微电子研究所, 北京 100029;3. 中国物联网研究发展中心, 江苏无锡 214135;4. 上海电力学院电子与信息工程学院, 上海 200090

摘要：	提出一种基于潜在概率语义(PLSA)模型和K近邻分类器的音频分类算法.首先,将信号特征向量送入潜在概率语义模型中训练,获得声音主题词袋模型;然后,使用K近邻分类器(KNN)进行分类.实验结果表明:与传统的K近邻分类算法相比,提出的算法在分类效果上有较明显的改善.
关键词：	梅尔频率倒谱系数词-频共现矩阵声音主题词袋模型潜在概率语义模型 K近邻分类器
Audio Classification Algorithm Using Probabilistic Latent Semantic Models and K Nearest Neighbor Classifier

XIN Xin^{1,' target="_blank" rel="external">3},CHEN Shudong^{2,' target="_blank" rel="external">3},TONG Minglei⁴,HU Wenhao^{1,' target="_blank" rel="external">3},LIU Chenwei^{1,' target="_blank" rel="external">3},GE Haodong³. Audio Classification Algorithm Using Probabilistic Latent Semantic Models and K Nearest Neighbor Classifier[J]. Journal of Huaqiao University(Natural Science), 2016, 0(2): 196-200. DOI: 10.11830/ISSN.1000-5013.2016.02.0196

Authors:	XIN Xin^{1,' target="} _blank" rel=" external" >3,CHEN Shudong^{2,' target="} _blank" rel=" external" >3,TONG Minglei⁴,HU Wenhao^{1,' target="} _blank" rel=" external" >3,LIU Chenwei^{1,' target="} _blank" rel=" external" >3,GE Haodong³

Affiliation:	1. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China; 2. Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; 3. China R& D Center for Interne

Abstract:	The paper proposed an audio classification algorithm based on probabilistic latent semantic analysis model(PLSA)and K-nearest neighbor classifiers(KNN). The algorithm first feed the audio signal feature vector into the PLSA model training to get a bag of sound frames models, then classify with the KNN classifier. Experimental results showed that the proposed classification algorithm has better classification effect compared with the traditional KNN algorithm.

Keywords:	Mel frequency cepstral coefficients word-frequency of co-occurrence matrix bag of sound frames models probabilistic latent semantic analysis model K-nearest neighbor classifiers
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