System for Automatic Singing Voice Recognition

Zwan P, Kostek B. System for Automatic Singing Voice Recognition. J Audio Eng Soc. 2008;56(9):710-723. Available from: https://aes.org/publications/elibrary-page/?id=14634

@article{Zwan2008_14634,
  author = {Zwan, Pawel and Kostek, Bozena},
  title = {{System for Automatic Singing Voice Recognition}},
  journal = {Journal of the Audio Engineering Society},
  volume = {56},
  number = {9},
  pages = {710--723},
  year = {2008},
  month = sep,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=14634}
}

TY  - JOUR
TI  - System for Automatic Singing Voice Recognition
AU  - Zwan, Pawel
AU  - Kostek, Bozena
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 56
IS  - 9
SP  - 710
EP  - 723
PY  - 2008
DA  - 2008/09/06
UR  - https://aes.org/publications/elibrary-page/?id=14634
PB  - Audio Engineering Society
LA  - en
AB  - A neural network was trained and tested to provide automated classification of singing voices, both recognizing voice quality (amateur, semiprofessional, and professional) and voice type (bass, baritone, tenor, alto, mezzo-soprano, and soprano). Parameters related to singing were defined to form feature vectors. Single vowel samples for each singer were judged by six experts to establish a quality index. In a test based on a database of 2690 samples, 90% of the decisions were correct. These results show that it is possible to use neural networks to create an expert system to evaluate singing.
ER  -