Extract fundamental frequency based on CNN combined with PYIN

TL;DR

This paper presents a combined approach using CNN and PYIN algorithms to improve the accuracy of fundamental frequency extraction in monophonic and polyphonic music, demonstrating superior performance over individual methods.

Contribution

The paper introduces a novel hybrid method that integrates CNN and PYIN algorithms for more accurate F0 extraction in musical signals.

Findings

Combined model outperforms individual algorithms in F0 accuracy

Hybrid approach improves F0 extraction in polyphonic music

Method evaluated on violin recordings with positive results

Abstract

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.