{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/34281"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/34281","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Online parameter selection for source separation using non-negative matrix factorization","abstract":"Blind source separation has been an area of study recently due to the many applications that might benefit from a good blind source separation algo- rithm. One instance is using blind source separation for audio denoising in cellular phones. In almost all instances, we have very little, if any, infor- mation about how background noise is mixed with the speaker’s voice in a given cell phone conversation. Current techniques include spectral subtrac- tion and Wiener filtering which are classical DSP techniques to deal with stationary noises. In this document, we aim to present a study on how to use blind source separation algorithms to denoise audio mixtures containing speech and various background noises. We mainly focus on how to imple- ment an online source separation algorithm which can handle non-stationary noises. To address the implementation, we also present a study on how to select the parameters in the separation algorithm in order to deliver the best performance for denoising using a statistical metric we have defined.","abstract_html":"Blind source separation has been an area of study recently due to the many applications that might benefit from a good blind source separation algo- rithm. One instance is using blind source separation for audio denoising in cellular phones. In almost all instances, we have very little, if any, infor- mation about how background noise is mixed with the speaker’s voice in a given cell phone conversation. Current techniques include spectral subtrac- tion and Wiener filtering which are classical DSP techniques to deal with stationary noises. In this document, we aim to present a study on how to use blind source separation algorithms to denoise audio mixtures containing speech and various background noises. We mainly focus on how to imple- ment an online source separation algorithm which can handle non-stationary noises. 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Current techniques include spectral subtrac- tion and Wiener filtering which are classical DSP techniques to deal with stationary noises. In this document, we aim to present a study on how to use blind source separation algorithms to denoise audio mixtures containing speech and various background noises. We mainly focus on how to imple- ment an online source separation algorithm which can handle non-stationary noises. To address the implementation, we also present a study on how to select the parameters in the separation algorithm in order to deliver the best performance for denoising using a statistical metric we have defined.","Item withdrawn by Rebecca Bryant (rabryant@illinois.edu) on 2012-07-10T20:07:19Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Kang_Kang.pdf: 1227057 bytes, checksum: 8e7f826826e1487a68ac0850062444eb (MD5)","Made available in DSpace on 2012-09-18T21:09:22Z (GMT). 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