文献类型：期刊文献

中文题名：面向翻唱歌曲识别的改进相似度网络融合算法

英文题名：A modified similarity network fusion algorithm for cover song identification

作者：朱东辉[1];陈宁[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2023

卷号：37

期号：1

起止页码：158

中文期刊名：重庆理工大学学报（自然科学）

外文期刊名：Journal of Chongqing University of Technology：Natural Science

收录：CSTPCD;;北大核心:【北大核心2020】；

基金：国家自然科学基金面上项目(61771196)。

语种：中文

中文关键词：翻唱歌曲识别;相似度网络融合;核矩阵

外文关键词：cover song identification;similarity network fusion;kernel matrix

摘要：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.