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An Approach for Vocal Register Recognition Based on Spectral Analysis of Singing
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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1128825Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 985
 J. Large, “Towards an integrated physiologic-acoustic theory of vocal registers,” The NATS Bulletin, vol. 28, pp. 30–35, 1972.
 R. L. Whitehead, D. E. Metz, and B. H. Whitehead, “Vibratory patterns of the vocal folds during pulse register phonation,” The Journal of the Acoustical Society of America, vol. 75, no. 4, pp. 1293–1297, Apr. 1984.
 J. Stark, Bel Canto: A History of Vocal Pedagogy. University of Toronto Press, 2003.
 R. H. Colton, J. K. Casper, and R. Leonard, Understanding Voice Probems: A Physiological Perspective for Diagnosis and Treatment. Lippincott Williams & Wilkins, 2006.
 A. Frisell, The Tenor voice: a personal guide to acquring a superior singing technique. Branden Publishing Company, 2007.
 G. J. Mysore, R. J. Cassidy, and J. O. Smith, “Singer-dependent falsetto detection for live vocal processing based on support vector classification,” in 2006 Fortieth Asilomar Conference on Signals, Systems and Computers. Institute of Electrical and Electronics Engineers (IEEE), 2006.
 C. T. Ishi, K.-I. Sakakibara, H. Ishiguro, and N. Hagita, “A method for automatic detection of vocal fry,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 16, no. 1, pp. 47–56, Jan. 2008.
 A. V. Oppenheim, R. W. Schafer, and J. R. Buck, Discrete-time Signal Processing (2nd Ed.). Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 1999.
 B. S. Manjunath, P. Salembier, and T. Sikora, Introduction to MPEG-7: Multimedia Content Description Interface. Wiley & Sons, 2002.
 C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995.
 S. Arlot and A. Celisse, “A survey of cross-validation procedures for model selection,” Statistics Surveys, vol. 4, pp. 40–79, 2010.