提出使用最小二乘支持向量机LS-SVM(Least Squares Support VectorMachines)算法进行乐器音乐分类,从而实现乐器的辩识。在对LS-SVM理论进行深入探讨的基础上,选择乐器音乐clip作为样本,进行特征提取,提取的特征包括频谱特征,短时自相关系数和MFCC等,然后用最小二乘支持向量机算法进行分类。对古琴、古筝、箜篌和琵琶音乐采取样本进行仿真实验,求得分类准确率和运行时间,同时使用逻辑回归(Logistic Regression)算法进行对比试验,其中最小二乘支持向量机和逻辑回归分类的准确率分别为96.5%和92.5%,且LS-SVM的运行时间比Logist的少。实验结果表明最小二乘支持向量机具有更为优越的分类性能和非线性处理能力,可以推广用于解决其它实际分类问题。
A novel regularization-based approach is presented for super-resolution reconstruction in order to achieve good tradeoff between noise removal and edge preservation.The method is developed by using L1 norm as data fidelity term and anisotropic fourth-order diffusion model as a regularization item to constrain the smoothness of the reconstructed images.To evaluate and prove the performance of the proposed method,series of experiments and comparisons with some existing methods including bi-cubic interpolation method and bilateral total variation method are carried out.Numerical results on synthetic data show that the PSNR improvement of the proposed method is approximately 1.0906 dB on average compared to bilateral total variation method,and the results on real videos indicate that the proposed algorithm is also effective in terms of removing visual artifacts and preserving edges in restored images.
