Date of Award
Summer 8-2017
Degree Type
Thesis
Degree Name
M.S.
Degree Program
Electrical Engineering
Department
Electrical Engineering
Major Professor
Dimitrios Charalampidis
Second Advisor
Vesselin Jilkov
Third Advisor
Kim D. Jovanovich
Abstract
Extraction of singing voice from music is one of the ongoing research topics in the field of speech recognition and audio analysis. In particular, this topic finds many applications in the music field, such as in determining music structure, lyrics recognition, and singer recognition. Although many studies have been conducted for the separation of voice from the background, there has been less study on singing voice in particular.
In this study, efforts were made to design a new methodology to improve the separation of vocal and non-vocal components in audio clips using REPET [14]. In the newly designed method, we tried to rectify the issues encountered in the REPET method, while designing an improved repeating mask which is used to extract the non-vocal component in audio. The main reason why the REPET method was preferred over previous methods for this study is its independent nature. More specifically, the majority of existing methods for the separation of singing voice from music were constructed explicitly based on one or more assumptions.
Recommended Citation
Kanuri, Mohan Kumar, "Separation of Vocal and Non-Vocal Components from Audio Clip Using Correlated Repeated Mask (CRM)" (2017). University of New Orleans Theses and Dissertations. 2381.
https://scholarworks.uno.edu/td/2381
Rights
The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.