中文摘要：

提出了一种利用训练数据的类别信息改善分类效果的音频特征提取方法．与传统的利用独立分量分析进行特征提取的方法相比，在计算训练向量空间的基函数组时，特征向量各分量之间的互信息不是直接在全体训练向量上计算的，而是分别在各个不同类的训练向量上计算，然后求其统计平均值．实验结果表明，用这种方法得到的基函数组，能够进一步减小同一类音频的特征向量各分量之间的互信息．从而提高分类的成功率．

英文摘要：

An audio feature extraction method which can improve classification rate by utilizing the class information is proposed in this paper. Contrary to the traditional feature extraction method based on independent component analysis, the mutual information among the dimensions of thc feature vectors is computed on each set of data belonging to different class, rather than on the whole training set. The average then is adopted as the contrast function when generating the basic functions of the training space. Experiments show that higher classification rate can be achieved on the new feature vectors extracted by this method, and that the new basic functions produce smaller mutual information among the dimensions of the feature vectors belonging to the same class.