학술논문

A comparative study of pitch extraction algorithms on a large variety of singing sounds
Document Type
Conference
Source
2013 IEEE International Conference on Acoustics, Speech and Signal Processing Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. :7815-7819 May, 2013
Subject
Signal Processing and Analysis
Speech
Hidden Markov models
Databases
Reverberation
Robustness
Estimation
Speech processing
singing analysis/synthesis
pitch extraction
Language
ISSN
1520-6149
2379-190X
Abstract
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.