面向翻唱歌曲识别的改进相似度网络融合算法

A modified similarity network fusion algorithm for cover song identification

提出了改进型相似度网络融合(modified similarity network fusion, MSNF)算法,通过自建核矩阵实现了非方阵的直接融合;通过引入核矩阵的融合,避免了由表现较差的特征构造的核矩阵的负面影响的延伸。在3个数据集上的实验结果表明:MSNF算法在翻唱歌曲识别任务中取得了比SNF算法更高的识别准确率,大幅度降低了时间复杂度。

This paper proposes the Modified Similarity Network Fusion(MSNF) algorithm, which realizes a direct fusion of non-square matrices by introducing self-built kernel matrices. At the same time, the kernel matrices are fused in advance to avoid the extension of negative influences caused by the kernel matrices constructed based on poorly behaved features. Experimental results on the three datasets demonstrate that the MSNF algorithm achieves higher recognition accuracy than the Similarity Network Fusion algorithm in cover song identification tasks, and greatly reduces the time complexity at the same time.