Open AccessSinger Identification using Autocorrelation Method
Author(s) -
Sharmila Biswas,
Sandeep Singh Solanki
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c4672.119420
Subject(s) - autocorrelation , hindi , identification (biology) , speech recognition , set (abstract data type) , test (biology) , mathematics , computer science , artificial intelligence , statistics , paleontology , botany , biology , programming language
songs are the compositions embedding voice and different instrument’s sound. Different human emotions can be created by playing the appropriate song .autocorrelation algorithm is used here to find out singer identification. In the first experiment three singers with three hindi songs (vocal) are taken as data set. Tempo is used as musical features. Then autocorrelation is proposed on concerning a total of three singers. Using bartlett test we have found the most significant autocorrelation values of those songs of three singers. In second experiment three singers with one hindi song (vocal) are taken as data set. Here rms is used as musical features. Then autocorrelation is proposed on concerning those three singers. Using bartlett test we have found the insignificant autocorrelation values of the song of three singers. The first experiment is used to identify the singers for each song. Here three singers identify their own identification test giving most significant values of their songs .the second experiment gives the insignificant value. The insignificance values of musical features of three singers does not give the singer’s identification test.