一种自适应谐波叠加的复调音乐主旋律提取新方法

随着音乐数量的迅速增加,对音乐进行数字化的处理已经成为必然趋势.主旋律反映了音乐的主要思想,提取主旋律在制作计算机音乐,检索分类,哼唱识别等领域具有广泛的应用价值.本文提出一种自适应谐波叠加的复调音乐主旋律提取算法.首先,通过声源分离预处理,通过判别基频最小稳定方差改变压缩因子,自适应叠加谐波构建显著函数;然后,对显著函数构建的基频片断采用随机森林模型进行人声检测,组合所有人声帧的最大显著度频率得到音乐的主旋律序列.实验表明,在MIR-1K数据集上得到的结果在高信噪比情况下有显著提升.

A New Method of Melody Extraction for Polyphonic Music based on Adaptive Harmonic Superposition

With the rapid increase in thevolume of music, digital processing of music has become an inevitable trend. Melody reflects the main ideas of music, melody extraction has wide application in computer music production, music retrieval and classification, and humming recognition and other fields. In this paper, a music melody extraction algorithm is proposed for polyphonic music based on adaptive harmonic superposition. A saliency function is first constructed by adaptive harmonic superposition through the preprocess of harmonic percussive source separation and the change of compression factor by discriminating the minimum stable variance of fundamental frequency. Then, random forest model is used to voice detection in the fundamental frequency segment constructed by the saliency function. Combining the frequency with maximum significance to get the melody sequence of music. Experiments show that the results obtained from the MIR 1K data set are significantly improved in the case of high signal to noise ratio.