{"_buckets": {"deposit": "e4af87ab-17bb-486c-bc78-2c097c30106d"}, "_deposit": {"id": "8678", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "8678"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00008678"}, "item_18_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2006-12", "bibliographicIssueDateType": "Issued"}, "bibliographicPageEnd": "44", "bibliographicPageStart": "39", "bibliographic_titles": [{"bibliographic_title": "4th Symposium on \"Intelligent Media Integration for Social Information Infrastructure\" December 7-8, 2006"}]}]}, "item_18_description_4": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "In this paper we deal with the blind source separation (BSS) problem in the frequency domain. To solve this problem we adopt the idea of super-exponential methods (SEM) and eigenvector algorithms (EVA) with reference signals. SEMs have the attractive property that they are computationally efficient and converge to the desired solutions at a super-exponential rate. Conventional SEMs have the problem that they are sensitive to Gaussian noise. To overcome this issue, we propose the robust SEM, which utilize only the higher-order cumulants. SEMs, however, have the drawback that they fail to extract all the source signals due to the failure of the deflation process. To compensate for this drawback, we propose the EVA with reference signals, which makes it possible to extract all the sources.", "subitem_description_type": "Abstract"}]}, "item_18_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/10429"}]}, "item_18_publisher_32": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "INTELLIGENT MEDIA INTEGRATION  NAGOYA UNIVERSITY / COE"}]}, "item_18_select_15": {"attribute_name": "\u8457\u8005\u7248\u30d5\u30e9\u30b0", "attribute_value_mlt": [{"subitem_select_item": "publisher"}]}, "item_18_text_14": {"attribute_name": "\u30d5\u30a9\u30fc\u30de\u30c3\u30c8", "attribute_value_mlt": [{"subitem_text_value": "application/pdf"}]}, "item_creator": {"attribute_name": "\u8457\u8005", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Ito, Masanori"}], "nameIdentifiers": [{"nameIdentifier": "24406", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Kawamoto, Mitsuru"}], "nameIdentifiers": [{"nameIdentifier": "24407", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Ohnishi, Noboru"}], "nameIdentifiers": [{"nameIdentifier": "24408", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Inouye, Yujiro"}], "nameIdentifiers": [{"nameIdentifier": "24409", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "\u30d5\u30a1\u30a4\u30eb\u60c5\u5831", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2018-02-19"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "p39-44_Super_exponential_algorithms.pdf", "filesize": [{"value": "376.7 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 376700.0, "url": {"label": "p39-44_Super_exponential_algorithms.pdf", "url": "https://nagoya.repo.nii.ac.jp/record/8678/files/p39-44_Super_exponential_algorithms.pdf"}, "version_id": "099bbd7a-8587-4a2e-9d78-440f57591d99"}]}, "item_language": {"attribute_name": "\u8a00\u8a9e", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"resourcetype": "conference paper", "resourceuri": "http://purl.org/coar/resource_type/c_5794"}]}, "item_title": "SUPER-EXPONENTIAL ALGORITHMS AND EIGENVECTOR ALGORITHMS FOR BLIND SOURCE SEPARATIOIN", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "SUPER-EXPONENTIAL ALGORITHMS AND EIGENVECTOR ALGORITHMS FOR BLIND SOURCE SEPARATIOIN", "subitem_title_language": "en"}]}, "item_type_id": "18", "owner": "1", "path": ["312/1037/1038"], "permalink_uri": "http://hdl.handle.net/2237/10429", "pubdate": {"attribute_name": "PubDate", "attribute_name_i18n": "\u516c\u958b\u65e5", "attribute_value": "2008-08-22"}, "publish_date": "2008-08-22", "publish_status": "0", "recid": "8678", "relation": {}, "relation_version_is_last": true, "title": ["SUPER-EXPONENTIAL ALGORITHMS AND EIGENVECTOR ALGORITHMS FOR BLIND SOURCE SEPARATIOIN"], "weko_shared_id": null}