Open Access 
Downloads
Abstract
Our ears often simultaneously receive various sound sources (speech, music, noise . . .), but we can still listen to the intended sound. A system of speech recognition must be able to achieve the same intelligent level. The problem is that we receive many mixed (combined) signals from many different source signals, and would like to recover them separately. This is the problem of Blind Source Separation (BSS). In the last decade or so a method has been developed to solve the above problem effectively, that is the Independent Component Analysis (ICA). There are many ICA algorithms for different applications. This report describes our application to sound separation when there are more sources than mixtures (underdetermined case). The results were quite good.
Issue: Vol 14 No 4 (2011)
Page No.: 34-42
Published: Dec 30, 2011
Section: Natural Sciences - Research article
DOI: https://doi.org/10.32508/stdj.v14i4.2034
Copyright: The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Download PDF = 588 times
Total = 588 times
Most read articles by the same author(s)
- Khoa Le Dang, Tung Thanh Vu, Tu Thanh Nguyen, Phuong Huu Nguyen, Indoor optical wireless MIMO-OFDM system , Science and Technology Development Journal: Vol 17 No 1 (2014)
- Tu Thanh Nguyen, Khoa Le Dang, Thu Thi Hong Nguyen, Phuong Huu Nguyen, Spectrum sensing based on energy of unknown deterministic signals over fading channels , Science and Technology Development Journal: Vol 17 No 1 (2014)
