Audio Feature Optimization based on the PSO and Attribute Importance
This paper presents a novel approach to achieve optimization for the audio features in compressed domain, which is the PSO (particle swarm optimization) algorithm basing on the attribute importance criterion of rough set theory. Our method firstly extracts the attributes of audio to form the feature vectors and pre-processes these vectors, then realizes the optimization using the proposed PSO algorithm, and finally determines the optimal feature subset. The experimental results show that feature optimization not only greatly reduces the training time of classifier, but also improves the classification accuracy. The performance of the classification model developed on the optimal feature subset. It achieves effective dimensionality reduction.
Wei Yang Xiaoqing Yu Junwei Liu Changlian Li Wanggen Wan
School of Communication and Information Engineering, Shanghai University Shanghai 200072, China
国际会议
上海
英文
705-709
2010-10-20（万方平台首次上网日期，不代表论文的发表时间）