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
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;
Conference_Titel :
Communications (NCC), 2012 National Conference on
Conference_Location :
Kharagpur
Print_ISBN :
978-1-4673-0815-1
DOI :
10.1109/NCC.2012.6176860