An unified approach for blind source separation using sparsity and decorrelation. Feng, F. & Kowalski, M. In 2015 23rd European Signal Processing Conference (EUSIPCO), pages 1736-1740, Aug, 2015.
![An unified approach for blind source separation using sparsity and decorrelation [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper doi abstract bibtex
Paper doi abstract bibtex Independent component analysis (ICA) has been a major tool for blind source separation (BSS). Both theoretical and practical evaluations showed that the hypothesis of independence suits well for audio signals. In the last few years, optimization approach based on sparsity has emerged as another efficient implement for BSS. This paper starts from introducing some new BSS methods that take advantages of both decorrelation (which is a direct consequence of independence) and sparsity using overcomplete Gabor representation. It is shown that the proposed methods work in both under-determined and over-determined cases. Experimental results illustrate the good performances of these approaches for audio mixtures.
@InProceedings{7362681,
author = {F. Feng and M. Kowalski},
booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
title = {An unified approach for blind source separation using sparsity and decorrelation},
year = {2015},
pages = {1736-1740},
abstract = {Independent component analysis (ICA) has been a major tool for blind source separation (BSS). Both theoretical and practical evaluations showed that the hypothesis of independence suits well for audio signals. In the last few years, optimization approach based on sparsity has emerged as another efficient implement for BSS. This paper starts from introducing some new BSS methods that take advantages of both decorrelation (which is a direct consequence of independence) and sparsity using overcomplete Gabor representation. It is shown that the proposed methods work in both under-determined and over-determined cases. Experimental results illustrate the good performances of these approaches for audio mixtures.},
keywords = {audio signals;blind source separation;decorrelation;Gabor filters;independent component analysis;optimisation;blind source separation;unified approach;sparsity;decorrelation;independent component analysis;ICA;audio signals;optimization approach;direct independence consequence;overcomplete Gabor representation;audio mixtures;Decorrelation;Signal processing algorithms;Convergence;Optimization;Signal to noise ratio;Europe;Blind Source Separation;Sparsity;Independant Component Analysis;Optimization},
doi = {10.1109/EUSIPCO.2015.7362681},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570103191.pdf},
}Downloads: 0
{"_id":"mTxRv86C7TBeLz9bN","bibbaseid":"feng-kowalski-anunifiedapproachforblindsourceseparationusingsparsityanddecorrelation-2015","authorIDs":[],"author_short":["Feng, F.","Kowalski, M."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["F."],"propositions":[],"lastnames":["Feng"],"suffixes":[]},{"firstnames":["M."],"propositions":[],"lastnames":["Kowalski"],"suffixes":[]}],"booktitle":"2015 23rd European Signal Processing Conference (EUSIPCO)","title":"An unified approach for blind source separation using sparsity and decorrelation","year":"2015","pages":"1736-1740","abstract":"Independent component analysis (ICA) has been a major tool for blind source separation (BSS). Both theoretical and practical evaluations showed that the hypothesis of independence suits well for audio signals. In the last few years, optimization approach based on sparsity has emerged as another efficient implement for BSS. This paper starts from introducing some new BSS methods that take advantages of both decorrelation (which is a direct consequence of independence) and sparsity using overcomplete Gabor representation. It is shown that the proposed methods work in both under-determined and over-determined cases. Experimental results illustrate the good performances of these approaches for audio mixtures.","keywords":"audio signals;blind source separation;decorrelation;Gabor filters;independent component analysis;optimisation;blind source separation;unified approach;sparsity;decorrelation;independent component analysis;ICA;audio signals;optimization approach;direct independence consequence;overcomplete Gabor representation;audio mixtures;Decorrelation;Signal processing algorithms;Convergence;Optimization;Signal to noise ratio;Europe;Blind Source Separation;Sparsity;Independant Component Analysis;Optimization","doi":"10.1109/EUSIPCO.2015.7362681","issn":"2076-1465","month":"Aug","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570103191.pdf","bibtex":"@InProceedings{7362681,\n author = {F. Feng and M. Kowalski},\n booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},\n title = {An unified approach for blind source separation using sparsity and decorrelation},\n year = {2015},\n pages = {1736-1740},\n abstract = {Independent component analysis (ICA) has been a major tool for blind source separation (BSS). Both theoretical and practical evaluations showed that the hypothesis of independence suits well for audio signals. In the last few years, optimization approach based on sparsity has emerged as another efficient implement for BSS. This paper starts from introducing some new BSS methods that take advantages of both decorrelation (which is a direct consequence of independence) and sparsity using overcomplete Gabor representation. It is shown that the proposed methods work in both under-determined and over-determined cases. Experimental results illustrate the good performances of these approaches for audio mixtures.},\n keywords = {audio signals;blind source separation;decorrelation;Gabor filters;independent component analysis;optimisation;blind source separation;unified approach;sparsity;decorrelation;independent component analysis;ICA;audio signals;optimization approach;direct independence consequence;overcomplete Gabor representation;audio mixtures;Decorrelation;Signal processing algorithms;Convergence;Optimization;Signal to noise ratio;Europe;Blind Source Separation;Sparsity;Independant Component Analysis;Optimization},\n doi = {10.1109/EUSIPCO.2015.7362681},\n issn = {2076-1465},\n month = {Aug},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570103191.pdf},\n}\n\n","author_short":["Feng, F.","Kowalski, M."],"key":"7362681","id":"7362681","bibbaseid":"feng-kowalski-anunifiedapproachforblindsourceseparationusingsparsityanddecorrelation-2015","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570103191.pdf"},"keyword":["audio signals;blind source separation;decorrelation;Gabor filters;independent component analysis;optimisation;blind source separation;unified approach;sparsity;decorrelation;independent component analysis;ICA;audio signals;optimization approach;direct independence consequence;overcomplete Gabor representation;audio mixtures;Decorrelation;Signal processing algorithms;Convergence;Optimization;Signal to noise ratio;Europe;Blind Source Separation;Sparsity;Independant Component Analysis;Optimization"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2015url.bib","creationDate":"2021-02-13T17:31:52.492Z","downloads":0,"keywords":["audio signals;blind source separation;decorrelation;gabor filters;independent component analysis;optimisation;blind source separation;unified approach;sparsity;decorrelation;independent component analysis;ica;audio signals;optimization approach;direct independence consequence;overcomplete gabor representation;audio mixtures;decorrelation;signal processing algorithms;convergence;optimization;signal to noise ratio;europe;blind source separation;sparsity;independant component analysis;optimization"],"search_terms":["unified","approach","blind","source","separation","using","sparsity","decorrelation","feng","kowalski"],"title":"An unified approach for blind source separation using sparsity and decorrelation","year":2015,"dataSources":["eov4vbT6mnAiTpKji","knrZsDjSNHWtA9WNT"]}