Singer Identification using Autocorrelation Method
Sharmila Biswas1, Sandeep Singh Solanki2

1SharmilaBiswas PhD student in the Electronics and Communication Engineering Department, Birla Institute of Technology, Deemed University.
1Dr. Sandeep Singh Solanki Professor Electronics and Communication Engineering Department, Birla Institute of Technology, Deemed University

Manuscript received on October 06, 2020. | Revised Manuscript received on October 25, 2020. | Manuscript published on November 30, 2020. | PP: 34-38 | Volume-9 Issue-4, November 2020. | Retrieval Number: 100.1/ijrte.C4672099320 | DOI: 10.35940/ijrte.C4672.119420
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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. 
Keywords: Tempo, rms, autocorrelation, song.