This paper describes our work on automatic classification of phonation modes on singing voice. In the first part of the paper, we will briefly review the main characteristics of the different phonation modes. Then, we will describe the isolated vowels databases we used, with emphasis on a new database we recorded specifically for the purpose of this work. The next section will be dedicated to the description of the proposed set of parameters (acoustic and glottal) and the classification framework. The results obtained with only acoustic parameters are close to 80% of correct recognition, which seems sufficient for experimenting with continuous singing. Therefore, we set up two other experiments in order to see if the system may be of any practical use for singing voice characterisation. The first experiment aims at assessing if automatic detection of phonation modes may help classify singing into different styles. This experiment is carried out using a database of one singer singing the same song in 8 styles. The second experiment is carried out on field recordings from ethnomusicologists and concerns the distinction between “normal” singing and “laments” from a variety of countries.
Cite as: Rouas, J.-L., Ioannidis, L. (2016) Automatic Classification of Phonation Modes in Singing Voice: Towards Singing Style Characterisation and Application to Ethnomusicological Recordings. Proc. Interspeech 2016, 150-154, doi: 10.21437/Interspeech.2016-1135
@inproceedings{rouas16_interspeech,
author={Jean-Luc Rouas and Leonidas Ioannidis},
title={{Automatic Classification of Phonation Modes in Singing Voice: Towards Singing Style Characterisation and Application to Ethnomusicological Recordings}},
year=2016,
booktitle={Proc. Interspeech 2016},
pages={150--154},
doi={10.21437/Interspeech.2016-1135}
}