Blind source separation of acoustic mixtures using time-frequency domain independent component analysis

被引：0
|
作者：
Jayaraman, S [1 ]
Sitaraman, G [1 ]
Seshadri, R [1 ]
机构：
[1] PSG Coll Technol, Dept Elect & Commun Engn, Coimbatore 641004, Tamil Nadu, India
来源：
ICONIP'02: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING: COMPUTATIONAL INTELLIGENCE FOR THE E-AGE | 2002年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Blind Source Separation of acoustic mixtures aims at providing a solution to the classical cocktail-party problem. The inherent delays and convolutions in microphone recordings, entails a modification in the Independent Component Analysis (ICA), which achieves separation by making the assumption of statistical independence of source signals that are linearly combined. The Proposed Algorithm provides a solution for the blind source separation problem by shifting he domain of the problem to Time-Frequency domain and applying ICA to each of the frequency components individually. Satisfactory results were achieved for Speech-Music as well as Speech-Speech Separation by adopting the Time-Frequency domain ICA.
引用
收藏
页码：1383 / 1387
页数：5
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