  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.
