基于分形布朗运动和Ada Boosting的多类音频例子识别

Recognition of Multiple Audio Clip Classes Based on FBM and Ada Boosting

提出了一种基于分形布朗运动的音频特征提取和识别方法 这种方法使用分形布朗运动模型计算出音频例子的分形维数 ,并作为其分形特征 针对音频分形特征符合高斯分布的特点 ,使用AdaBoosting算法进行特征约减 然后分别使用Ada 加权高斯分类器和支持向量机对约减特征后的音频分类 ,并在两类分类的基础上构造多类分类的模型 实验表明 。

A novel method for audio feature extraction and recognition is presented In this method, FBM (fractional brownian motion) based fractal dimension is defined as audio fractal feature According to Gaussian distribution characteristic of audio fractal feature, Ada boosting algorithm is used for feature reduction Then two classifiers, weighted Ada Gaussian classifier and support vector machine, are implemented respectively for audio classification Based on these two classifiers, a multiple classifier model is finally constructed Experimental data shows that audio fractal feature achieves better performance than other audio features for music and speech classification