摘要
在音乐信息检索领域,主旋律的提取是一项非常困难的工作。复调音乐中的不同声源相互影响,导致主旋律音高序列不连续,使旋律原始音高准确率降低。针对这一问题,设计了增强音高显著性表示和自动旋律跟踪的CNN-CRF模型。为了更好地提取谐波信息,提出利用结构化的数据来加强SF-NMF计算的初始显著性表示,并在动态规划框架下结合旋律特征和音高的平滑约束条件在音高空间寻找最优的演变路径。实验表明,所提方法得到了较好的旋律提取结果,且在两个测试数据集上的原始音高准确率均高于其他参考方法,通过对比不同输入验证了结构化数据能加强显著性表示并弥补SF-NMF对音高的误判。
In the field of music information retrieval,the extraction of the main melody is a very difficult task.In the polyphonic music,different sound sources interact with each other,leading to discontinuity of the main melody’s pitch sequence,which reduces the accuracy of the original pitch of the melody.In response to this problem,a CNN-CRF model with enhanced pitch salie-ncy representation and automatic melody tracking is designed.In order to better extract the harmonic information,it is proposed to enhance the initial saliency representation of the SF-NMF calculation by structured data,and to combine the melody characte-ristics and the smooth constraint conditions of the pitch under the dynamic programming framework to find the optimal evolution path.Experiments show that the proposed method has better melody extraction results,and the original pitch accuracy on both test data sets is higher than that of other reference methods.Comparing different inputs validates that structured data can enhance the significance representation and make up for the misJudgement of pitch by SF-NMF.
作者
金文清
韩芳
JIN Wen-qing;HAN Fang(School of Information Science and Technology,Donghua University,Shanghai 201620,China)
出处
《计算机科学》
CSCD
北大核心
2020年第S01期24-28,共5页
Computer Science
基金
国家自然科学基金(11572084,11972115)。
