%0 Journal Article
	%A Aleksandra Zysk and  Pawel Badura
	%D 2017
	%J International Journal of Cognitive and Language Sciences
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 122, 2017
	%T An Approach for Vocal Register Recognition Based on Spectral Analysis of Singing
	%U https://publications.waset.org/pdf/10006407
	%V 122
	%X 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.
	%P 207 - 212