# A Comparative Study of Pitch Extraction Algorithms on a Large Variety of Singing Sounds

arXiv: https://arxiv.org/abs/1912.12609 · HTML: https://mlsanity.com/paper/1912.12609/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2019.

## Built on by (most cited first, 3 of 3)

- [Karaoker: Alignment-free singing voice synthesis with speech training data](https://mlsanity.com/paper/2204.04127/) (2022)
- [The Use of Voice Source Features for Sung Speech Recognition](https://mlsanity.com/paper/2102.10376/) (2021)
- [Adapitch: Adaption Multi-Speaker Text-to-Speech Conditioned on Pitch Disentangling with Untranscribed Data](https://mlsanity.com/paper/2210.13803/) (2022)

## Abstract (arXiv)

The problem of pitch tracking has been extensively studied in the speech research community. The goal of this paper is to investigate how these techniques should be adapted to singing voice analysis, and to provide a comparative evaluation of the most representative state-of-the-art approaches. This study is carried out on a large database of annotated singing sounds with aligned EGG recordings, comprising a variety of singer categories and singing exercises. The algorithmic performance is assessed according to the ability to detect voicing boundaries and to accurately estimate pitch contour. First, we evaluate the usefulness of adapting existing methods to singing voice analysis. Then we compare the accuracy of several pitch-extraction algorithms, depending on singer category and laryngeal mechanism. Finally, we analyze their robustness to reverberation.