@article{(Open Science Index):https://publications.waset.org/pdf/10006407,
	  title     = {An Approach for Vocal Register Recognition Based on Spectral Analysis of Singing},
	  author    = {Aleksandra Zysk and  Pawel Badura},
	  country	= {},
	  institution	= {},
	  abstract     = {Recognizing and controlling vocal registers during
singing is a difficult task for beginner vocalist. It requires among
others identifying which part of natural resonators is being used
when a sound propagates through the body. Thus, an application
has been designed allowing for sound recording, automatic vocal
register recognition (VRR), and a graphical user interface providing
real-time visualization of the signal and recognition results. Six
spectral features are determined for each time frame and passed to the
support vector machine classifier yielding a binary decision on the
head or chest register assignment of the segment. The classification
training and testing data have been recorded by ten professional
female singers (soprano, aged 19-29) performing sounds for both
chest and head register. The classification accuracy exceeded 93%
in each of various validation schemes. Apart from a hard two-class
clustering, the support vector classifier returns also information on
the distance between particular feature vector and the discrimination
hyperplane in a feature space. Such an information reflects the level
of certainty of the vocal register classification in a fuzzy way. Thus,
the designed recognition and training application is able to assess and
visualize the continuous trend in singing in a user-friendly graphical
mode providing an easy way to control the vocal emission.},
	    journal   = {International Journal of Cognitive and Language Sciences},
	  volume    = {11},
	  number    = {2},
	  year      = {2017},
	  pages     = {207 - 212},
	  ee        = {https://publications.waset.org/pdf/10006407},
	  url   	= {https://publications.waset.org/vol/122},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 122, 2017},