论文标题
基于CNN与Pyin结合的提取基本频率
Extract fundamental frequency based on CNN combined with PYIN
论文作者
论文摘要
本文是指基于Pyin的多个基本频率（多个F0），一种用于提取单声音乐基本频率（F0）的算法，以及受过训练的卷积神经网络（CNN）模型，该模型可产生输入信号的音高攻击性功能，以估算多个f0多个F0。本文讨论了这两种算法及其相应的优势和缺点的实施。为了分析这两种方法的不同性能，Pyin用于补充从训练有素的CNN模型中提取的F0，以结合这两种算法的优势。为了进行评估，使用了两个小提琴演奏的四件，并评估了模型的性能，以提取F0曲线的平坦度。结果表明，从单声音乐和复音音乐中提取F0时，组合模型的表现优于原始算法。
This paper refers to the extraction of multiple fundamental frequencies (multiple F0) based on PYIN, an algorithm for extracting the fundamental frequency (F0) of monophonic music, and a trained convolutional neural networks (CNN) model, where a pitch salience function of the input signal is produced to estimate the multiple F0. The implementation of these two algorithms and their corresponding advantages and disadvantages are discussed in this article. Analysing the different performance of these two methods, PYIN is applied to supplement the F0 extracted from the trained CNN model to combine the advantages of these two algorithms. For evaluation, four pieces played by two violins are used, and the performance of the models are evaluated accoring to the flatness of the F0 curve extracted. The result shows the combined model outperforms the original algorithms when extracting F0 from monophonic music and polyphonic music.
