• DocumentCode
    1661261
  • Title

    Assessing vowel quality for singing evaluation

  • Author

    Jha, Mayank Vibhuti ; Rao, Preeti

  • Author_Institution
    Dept. of Electr. Eng., Indian Inst. of Technol. Bombay, Mumbai, India
  • fYear
    2012
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    The proper pronunciation of lyrics is an important component of vocal music. While automatic vowel classification has been widely studied for speech, a separate investigation of the methods is needed for singing due to the differences in acoustic properties between sung and spoken vowels. Acoustic features combining spectrum envelope and pitch are used with classifiers trained on sung vowels for classification of test vowels segmented from the audio of solo singing. Two different classifiers are tested, viz., Gaussian Mixture Models (GMM) and Linear Regression, and observed to perform well on both male and female sung vowels.
  • Keywords
    Gaussian processes; audio signal processing; regression analysis; GMM; Gaussian mixture models; acoustic features; acoustic properties; automatic vowel classification; linear regression; lyric pronunciation; singing evaluation; solo singing audio; spectrum envelope; spoken vowels; sung vowels; test vowel segmentation; vocal music component; vowel quality; Databases; Linear regression; Mel frequency cepstral coefficient; Robustness; Support vector machine classification; Training; Vectors; GMM; Linear Regression; MFCC; Singing Voice; Vowel Classification; Vowel Quality;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communications (NCC), 2012 National Conference on
  • Conference_Location
    Kharagpur
  • Print_ISBN
    978-1-4673-0815-1
  • Type

    conf

  • DOI
    10.1109/NCC.2012.6176860
  • Filename
    6176860