@inproceedings{576c2af0c31b40a29392366cebf4bbf9,
title = "Speech enhancement using ICA with EMD-based reference",
abstract = "Different from the traditional ICA that recovers all the source signals simultaneously, the ICA with reference (ICA-R) extracts only some desired source signals from the mixtures of source signals by incorporating some a priori information into the separation process. This paper applies ICA-R to extracting a target speech signal from its noisy linear mixtures by constructing a proper reference signal with the empirical mode decomposition (EMD). Specifically, EMD is used to obtain an approximate envelope of the power spectrum of the desired speech, which is quite different from the power spectra of the environmental noises. The results of computer simulations and performance analyses demonstrate the efficiency of the proposed method.",
author = "Yongrui Zheng and Qiuhua Lin and Fuliang Yin and Hualou Liang",
year = "2006",
month = feb,
day = "13",
doi = "10.1007/11679363\_92",
language = "English",
isbn = "9783540326304",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "739--746",
editor = "Justinian Rosca and Deniz Erdogmus and Pr{\'i}ncipe, \{Jos{\'e} C.\} and Simon Haykin",
booktitle = "Independent Component Analysis and Blind Signal Separation",
address = "Germany",
note = "6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006 ; Conference date: 05-03-2006 Through 08-03-2006",
}