摘要
为了对音频信号进行有效的分类,提出一种在小波变换子空间中基于支持向量机和模糊积分进行信号特征提取和分类的新算法.首先,对信号进行预加重和窗化处理;其次,用小波变换把信号分解到不同的子空间并提取每个子空间的特征;再次,对每一个子空间信号特征向量进行标准化、降维和分类;最后,用模糊积分将子空间分类结果融合,得出最终类.试验表明本算法速度较快、精确度高.
In order to classify audio signals effectively, a new signal feature extraction and classification algorithm is proposed based on SVM （Support Vector Machine） and fuzzy integral in wavelet transform sub-spaces. Firstly, the signal is pre-emphasized and windowed. Then, signals are decomposed into different subspaces using wavelet transform, and features of each subspace are extracted. Thirdly, standardization and dimension reduction are performed to classify signals in each signal subspace. In the end, classification results of each subspace are fused by means of fuzzy integral to get the final class. Experiments show that the proposed algorithm has a faster speed, higher accuracy.
出处
《信息与控制》
CSCD
北大核心
2007年第2期211-217,共7页
Information and Control
关键词
小波子空间
支持向量机
特征提取
信号分类
模糊积分
wavelet subspace
SVM （ Support Vector Machine）
feature extraction
signal classification
fuzzy integral
