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F. Scholz, I. Vatolkin, and G. Rudolph, "Singing Voice Detection across Different Music Genres," Paper P2-1, (2017 June.). doi: F. Scholz, I. Vatolkin, and G. Rudolph, "Singing Voice Detection across Different Music Genres," Paper P2-1, (2017 June.). doi:
Abstract: Most of recent studies on vocal detection in audio recordings typically focus either on the development of new features or on classification methods. The impact of training and test data is largely neglected, leading to weaknesses in the design of databases which do not cover differences of vocal techniques across music genres. In this paper, we compare approaches for singing voice detection on individual genres. For both methods with the best performance, we further investigate the impact of disjunct distribution of training and test tracks with regard to their genres. In particular, the tracks of electronic genres, which are barely contained in public databases for vocal recognition, contribute to a better classification performance identifying vocals in tracks of other genres.

@article{scholz2017singing,
author={scholz, florian and vatolkin, igor and rudolph, günter},
journal={journal of the audio engineering society},
title={singing voice detection across different music genres},
year={2017},
volume={},
number={},
pages={},
doi={},
month={june},} @article{scholz2017singing,
author={scholz, florian and vatolkin, igor and rudolph, günter},
journal={journal of the audio engineering society},
title={singing voice detection across different music genres},
year={2017},
volume={},
number={},
pages={},
doi={},
month={june},
abstract={most of recent studies on vocal detection in audio recordings typically focus either on the development of new features or on classification methods. the impact of training and test data is largely neglected, leading to weaknesses in the design of databases which do not cover differences of vocal techniques across music genres. in this paper, we compare approaches for singing voice detection on individual genres. for both methods with the best performance, we further investigate the impact of disjunct distribution of training and test tracks with regard to their genres. in particular, the tracks of electronic genres, which are barely contained in public databases for vocal recognition, contribute to a better classification performance identifying vocals in tracks of other genres.},}

TY - paper
TI - Singing Voice Detection across Different Music Genres
SP - EP -
AU - Scholz, Florian
AU - Vatolkin, Igor
AU - Rudolph, Günter
PY - 2017
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - June 2017 TY - paper
TI - Singing Voice Detection across Different Music Genres
SP - EP -
AU - Scholz, Florian
AU - Vatolkin, Igor
AU - Rudolph, Günter
PY - 2017
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - June 2017
AB - Most of recent studies on vocal detection in audio recordings typically focus either on the development of new features or on classification methods. The impact of training and test data is largely neglected, leading to weaknesses in the design of databases which do not cover differences of vocal techniques across music genres. In this paper, we compare approaches for singing voice detection on individual genres. For both methods with the best performance, we further investigate the impact of disjunct distribution of training and test tracks with regard to their genres. In particular, the tracks of electronic genres, which are barely contained in public databases for vocal recognition, contribute to a better classification performance identifying vocals in tracks of other genres.