1. AbeBer, J., Hasselhorn, J., Grollmisch, S., Dittmar, C, Lehmann, A., 2014. Automatic competency assessment of rhythm performances of ninth-grade and tenth-grade pupils, in: 2014 International Computer Music Conference, pp. 1252–1256.
2. Bohm, J., Eyben, R, Schmitt, M., Kosch, H., Schuller, B., 2017. Seeking the superstar: Automatic assessment of perceived singing quality, in: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 1560–1569. doi:10.1109/IJCNN. 2017.7966037.
3. Bjorkner, E., 2006. Why so different? Aspects of voice characteristics in operatic and musical theatre singing. Ph.D. thesis. KTH School of Computer Science and Communication.
4. Devaney, J.C., Mandel, M.I., Fujinaga, I., 2011. Characterizing singing voice fundamental frequency trajectories, in: 2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pp. 73–76. doi: 10.1109/ASPAA. 2011.6082333.
5. Dittmar, C, AbeBer, J., Grollmisch, S., Lehmann, A., Hasselhorn, J., 2012. Automatic singing assessment of pupil performances, in: Proceedings of the 12th International Conference on Music Perception and Cognition (ICMPC) and 8th Conference of the European Society for the Cognitive Sciences of Music (ESCOM), pp. 263–264.