dc.contributor.advisor | Azhar, Hanif Bin | |
dc.contributor.author | Chowdhury, Mahpara Hyder | |
dc.date.accessioned | 2014-05-14T06:57:22Z | |
dc.date.available | 2014-05-14T06:57:22Z | |
dc.date.copyright | 2014 | |
dc.date.issued | 2014-04 | |
dc.identifier.other | ID 10101036 | |
dc.identifier.uri | http://hdl.handle.net/10361/3225 | |
dc.description | This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2014. | en_US |
dc.description | Cataloged from PDF version of thesis report. | |
dc.description | Includes bibliographical references (page 36). | |
dc.description.abstract | Traditionally for feature extraction, decomposition techniques such as Fourier
decomposition are used to capture signals. But those methods have some margins
like – it only works for linear and stationary data. On the other hand, in real world,
we found data that are in non-linear and non-stationary. EMD or Empirical Mode
Decomposition technique is a new approach introduced by Huang et al (1998) that
can take any complicated signal and decomposed it to IMF. It extracts the
amplitude and frequency information of a signal at a particular time. It is robust for
non-linear and non-stationary signal processing. In this paper, I am using EMD as
a new approach for gender identification based on speech signal. Gender
identification based on the voice of a speaker consists of detecting if a speech
signal is given by a male or female. Detecting the gender of a speaker has several
applications. | en_US |
dc.description.statementofresponsibility | Mahpara Hyder Chowdhury | |
dc.format.extent | 47 pages | |
dc.language.iso | en | en_US |
dc.publisher | BRAC University | en_US |
dc.rights | BRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. | |
dc.subject | Computer science and engineering | en_US |
dc.title | Speech based gender identification using empirical mode decomposition (EMD) | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, BRAC University | |
dc.description.degree | B. Computer Science and Engineering | |