Automatic singing quality recognition employing artificial neural networks

• Autorzy: Żwan P.
• Konferencja: 12th International Symposium on Sound and Vision Engineering and Mastering (ISSVEM'07), June 15-16, Gdansk, Poland
• Języki publikacji: EN

Abstrakty

EN

The aim of the paper is to determine how quality of a singing voice can be recognized automatically. For this purpose, a database of singing voice sounds with samples of voices of trained and untrained singers was created and is presented. The methods of a singing voice parameterization are shortly reviewed and a set of descriptors is outlined. Each of the presented samples is parameterized and judged by experts, and the resulting feature vectors and quality scores are then used to train an artificial neural network. A comparison between experts' judgments and automatic recognition results is performed. Finally, statistical methods are applied to prove that an artificial neural network is able to automatically determine the quality of a singing voice with the accuracy very similar to expert assessments. The paper includes the discussion of results and presents derived conclusions.

Słowa kluczowe

EN

singing voice; neural networks; automatic sound recognition

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2008
• Tom: Vol. 33, No. 1
• Strony: 65--71
• Opis fizyczny: Bibliogr. 13. poz., rys., tab.

Twórcy

autor

Żwan P.

• Gdańsk University of Technology, Multimedia Systems Department, Narutowicza 11/12, 80-952 Gdańsk, Poland,

Bibliografia

• [1] BLOOTHOOF G., The sound level of the singer’s formant in professional singing, J. Ac. Soc. Am., 79, 2028–2032 (1986).
• [2] DIAZ J. A., ROTHMAN H. B., Acoustic parameters for determining the differences between good and poor vibrato in singing, Proc. 17th International Congress on Acoustics, Rome, VIII, 110–111 (2001).
• [3] DOWNIE S., Music information retrieval, annual review of information science and technology, 37, 295–340 (2003).
• [4] GOUTTE C., Note on free launches and cross-validation, Neural Computation, 2000.
• [5] HARMA A., KARJALAINEN M., VALIMAKI V., LAINE U., Frequency-warped signal processing for audio applications, J. Audio Eng. Soc., 2000.
• [6] JOLIVEAU E., SMITH J., WOLFE J., Vocal tract resonances in singing: the soprano voice, J. Acoust. Soc. Am., 116, 2434–39 (2004).
• [7] KOSTEK B., Perception-based data processing in acoustics. applications to music information retrieval and psychophysiology of hearing, Springer Verlag, Series on Cognitive Technologies, Berlin, Heidelberg, New York 2005.
• [8] MILLER D. G., Formant Tuning in a Professional Baritone, Journal of Voice, 4, 231–237 (1990).
• [9] ROTHMAN H. B., Why we don’t like these singers, Proc. 17th International Congress on Acoustics, VIII, 114–115 (2001).
• [10] SUNDBERG J., The science of the singing voice, Northern Illinois University Press, Illinois 1987.
• [11] ŻWAN P., Expert system for automatic classification and quality assessment of singing voices, Proc. 121 AES Convention, USA, San Francisco 2006.
• [12] ŻWAN P., SZCZUKO P., KOSTEK B., CZY˙ZEWSKI A., Automatic singing voice recognition employing neural networks and rough sets, RSEISP, LNAI, Warszawa 2007.
• [13] ŻWAN P., The expert system for objectivization of singing voice judgements [in Polish], Ph.D. Thesis, Gda´nsk Univ. of Technology, Multimedia Systems Dept., 2007.