School of Art, North University of China, Taiyuan 030051, China.
Guizhou Normal College, Guiyang 550018, China.
J Environ Public Health. 2022 Aug 31;2022:1074174. doi: 10.1155/2022/1074174. eCollection 2022.
Main melody extraction and multi-pitch estimation are two important research topics in the MIR field. In this article, the SVM algorithm is used to analyze and discuss music melody extraction and multi-pitch estimation. In the part of multi-fundamental frequency extraction, this article first filters the song signal with equal loudness and weakens the energy of the high-frequency and low-frequency parts of the song signal. Thereafter, the multi-resolution short-time Fourier transform suitable for processing song signals is introduced. In addition, in order to avoid the sharp jump of the estimated melody pitch in the same note duration range, this article proposes a main melody extraction method combining the SVM algorithm with dynamic programming. In this article, more features are used to distinguish the pitch contour of vocal fundamental frequency from that of the nonvocal fundamental frequency, which does not only depend on energy or a certain feature. The experimental results show that the lowest octave error of this method is 1.46. Meanwhile, the recall rate of the algorithm can reach about 95%. This method not only improves the recall rate of the fundamental frequency of the human voice but also improves the recall rate and pitch accuracy rate of the whole main melody extraction system.
旋律提取和多音调估计是 MIR 领域的两个重要研究课题。本文使用 SVM 算法对音乐旋律提取和多音调估计进行分析和讨论。在多基频提取部分，本文首先对歌曲信号进行等响滤波，减弱歌曲信号的高低频部分的能量。然后，引入适合处理歌曲信号的多分辨率短时傅里叶变换。此外，为了避免在同一音符持续时间范围内估计的旋律音高出现急剧跳跃，本文提出了一种结合 SVM 算法和动态规划的主旋律提取方法。在本文中，使用了更多的特征来区分人声基频的音高轮廓和非人声基频的音高轮廓，而不仅仅依赖于能量或某个特征。实验结果表明，该方法的最低八度误差为 1.46。同时，该算法的召回率可以达到 95%左右。该方法不仅提高了人声基频的召回率，而且提高了整个主旋律提取系统的整体召回率和音高准确率。
