ログイン
言語:

WEKO3

  • トップ
  • ランキング
To
lat lon distance
To

Field does not validate



インデックスリンク

インデックスツリー

メールアドレスを入力してください。

WEKO

One fine body…

WEKO

One fine body…

アイテム

{"_buckets": {"deposit": "dabc7bd0-8990-461c-8e27-62b0853cacf8"}, "_deposit": {"id": "13157", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "13157"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00013157", "sets": ["314"]}, "author_link": ["41485", "41486", "41487"], "item_10_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2006-03-01", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "3", "bibliographicPageEnd": "1049", "bibliographicPageStart": "1040", "bibliographicVolumeNumber": "E89-D", "bibliographic_titles": [{"bibliographic_title": "IEICE transactions on information and systems", "bibliographic_titleLang": "en"}]}]}, "item_10_description_4": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "This study shows the effectiveness of using gamma distribution in the speech power domain as a more general prior distribution for the model-based speech enhancement approaches. This model is a super-set of the conventional Gaussian model of the complex spectrum and provides more accurate prior modeling when the optimal parameters are estimated. We develop a method to adapt the modeled distribution parameters from each actual noisy speech in a frame-by-frame manner. Next, we derive and investigate the minimum mean square error (MMSE) and maximum a posterior probability (MAP) estimations in different domains of speech spectral magnitude, generalized power and its logarithm, using the proposed gamma modeling. Finally, a comparative evaluation of the MAP and MMSE filters is conducted. As the MMSE estimations tend to more complicated using more general prior distributions, the MAP estimations are given in closed-form extractions and therefore are suitable in the implementation. The adaptive estimation of the modeled distribution parameters provides more accurate prior modeling and this is the principal merit of the proposed method and the reason for the better performance. From the experiments, the MAP estimation is recommended due to its high efficiency and low complexity. Among the MAP based systems, the estimation in log-magnitude domain is shown to be the best for the speech recognition as the estimation in power domain is superior for the noise reduction.", "subitem_description_language": "en", "subitem_description_type": "Abstract"}]}, "item_10_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "URI", "subitem_identifier_uri": "http://www.ieice.org/jpn/trans_online/index.html"}, {"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/15052"}]}, "item_10_publisher_32": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "Institute of Electronics, Information and Communication Engineers", "subitem_publisher_language": "en"}]}, "item_10_relation_43": {"attribute_name": "関連情報", "attribute_value_mlt": [{"subitem_relation_type": "isVersionOf", "subitem_relation_type_id": {"subitem_relation_type_id_text": "http://www.ieice.org/jpn/trans_online/index.html", "subitem_relation_type_select": "URI"}}]}, "item_10_select_15": {"attribute_name": "著者版フラグ", "attribute_value_mlt": [{"subitem_select_item": "publisher"}]}, "item_10_source_id_7": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "0916-8532", "subitem_source_identifier_type": "PISSN"}]}, "item_1615787544753": {"attribute_name": "出版タイプ", "attribute_value_mlt": [{"subitem_version_resource": "http://purl.org/coar/version/c_970fb48d4fbd8a85", "subitem_version_type": "VoR"}]}, "item_access_right": {"attribute_name": "アクセス権", "attribute_value_mlt": [{"subitem_access_right": "open access", "subitem_access_right_uri": "http://purl.org/coar/access_right/c_abf2"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "DAT, Tran Huy", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "41485", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "TAKEDA, Kazuya", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "41486", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "ITAKURA, Fumitada", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "41487", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "ファイル情報", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2018-02-20"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "431.pdf", "filesize": [{"value": "591.9 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_note", "mimetype": "application/pdf", "size": 591900.0, "url": {"label": "431.pdf", "objectType": "fulltext", "url": "https://nagoya.repo.nii.ac.jp/record/13157/files/431.pdf"}, "version_id": "9ea40467-842a-48fd-aa47-e2b557ffb63f"}]}, "item_keyword": {"attribute_name": "キーワード", "attribute_value_mlt": [{"subitem_subject": "speech enhancement", "subitem_subject_scheme": "Other"}, {"subitem_subject": "speech recognition", "subitem_subject_scheme": "Other"}, {"subitem_subject": "gamma modeling", "subitem_subject_scheme": "Other"}, {"subitem_subject": "fourth-order moment", "subitem_subject_scheme": "Other"}, {"subitem_subject": "MMSE", "subitem_subject_scheme": "Other"}, {"subitem_subject": "MAP", "subitem_subject_scheme": "Other"}, {"subitem_subject": "spectral magnitude", "subitem_subject_scheme": "Other"}, {"subitem_subject": "power", "subitem_subject_scheme": "Other"}, {"subitem_subject": "log-spectral magnitude", "subitem_subject_scheme": "Other"}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "資源タイプ", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "Gamma Modeling of Speech Power and Its On-Line Estimation for Statistical Speech Enhancement", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "Gamma Modeling of Speech Power and Its On-Line Estimation for Statistical Speech Enhancement", "subitem_title_language": "en"}]}, "item_type_id": "10", "owner": "1", "path": ["314"], "permalink_uri": "http://hdl.handle.net/2237/15052", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2011-07-07"}, "publish_date": "2011-07-07", "publish_status": "0", "recid": "13157", "relation": {}, "relation_version_is_last": true, "title": ["Gamma Modeling of Speech Power and Its On-Line Estimation for Statistical Speech Enhancement"], "weko_shared_id": -1}
  1. A500 情報学部/情報学研究科・情報文化学部・情報科学研究科
  2. A500a 雑誌掲載論文
  3. 学術雑誌

Gamma Modeling of Speech Power and Its On-Line Estimation for Statistical Speech Enhancement

http://hdl.handle.net/2237/15052
http://hdl.handle.net/2237/15052
2eca6e21-af80-4edb-a785-237cf503d0ad
名前 / ファイル ライセンス アクション
431.pdf 431.pdf (591.9 kB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2011-07-07
タイトル
タイトル Gamma Modeling of Speech Power and Its On-Line Estimation for Statistical Speech Enhancement
言語 en
著者 DAT, Tran Huy

× DAT, Tran Huy

WEKO 41485

en DAT, Tran Huy

Search repository
TAKEDA, Kazuya

× TAKEDA, Kazuya

WEKO 41486

en TAKEDA, Kazuya

Search repository
ITAKURA, Fumitada

× ITAKURA, Fumitada

WEKO 41487

en ITAKURA, Fumitada

Search repository
アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
キーワード
主題Scheme Other
主題 speech enhancement
キーワード
主題Scheme Other
主題 speech recognition
キーワード
主題Scheme Other
主題 gamma modeling
キーワード
主題Scheme Other
主題 fourth-order moment
キーワード
主題Scheme Other
主題 MMSE
キーワード
主題Scheme Other
主題 MAP
キーワード
主題Scheme Other
主題 spectral magnitude
キーワード
主題Scheme Other
主題 power
キーワード
主題Scheme Other
主題 log-spectral magnitude
抄録
内容記述 This study shows the effectiveness of using gamma distribution in the speech power domain as a more general prior distribution for the model-based speech enhancement approaches. This model is a super-set of the conventional Gaussian model of the complex spectrum and provides more accurate prior modeling when the optimal parameters are estimated. We develop a method to adapt the modeled distribution parameters from each actual noisy speech in a frame-by-frame manner. Next, we derive and investigate the minimum mean square error (MMSE) and maximum a posterior probability (MAP) estimations in different domains of speech spectral magnitude, generalized power and its logarithm, using the proposed gamma modeling. Finally, a comparative evaluation of the MAP and MMSE filters is conducted. As the MMSE estimations tend to more complicated using more general prior distributions, the MAP estimations are given in closed-form extractions and therefore are suitable in the implementation. The adaptive estimation of the modeled distribution parameters provides more accurate prior modeling and this is the principal merit of the proposed method and the reason for the better performance. From the experiments, the MAP estimation is recommended due to its high efficiency and low complexity. Among the MAP based systems, the estimation in log-magnitude domain is shown to be the best for the speech recognition as the estimation in power domain is superior for the noise reduction.
言語 en
内容記述タイプ Abstract
出版者
言語 en
出版者 Institute of Electronics, Information and Communication Engineers
言語
言語 eng
資源タイプ
資源タイプresource http://purl.org/coar/resource_type/c_6501
タイプ journal article
出版タイプ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
関連情報
関連タイプ isVersionOf
識別子タイプ URI
関連識別子 http://www.ieice.org/jpn/trans_online/index.html
ISSN
収録物識別子タイプ PISSN
収録物識別子 0916-8532
書誌情報 en : IEICE transactions on information and systems

巻 E89-D, 号 3, p. 1040-1049, 発行日 2006-03-01
著者版フラグ
値 publisher
URI
識別子 http://www.ieice.org/jpn/trans_online/index.html
識別子タイプ URI
URI
識別子 http://hdl.handle.net/2237/15052
識別子タイプ HDL
戻る
0
views
See details
Views

Versions

Ver.1 2021-03-01 18:37:10.017274
Show All versions

Share

Mendeley Twitter Facebook Print Addthis

Cite as

エクスポート

OAI-PMH
  • OAI-PMH JPCOAR
  • OAI-PMH DublinCore
  • OAI-PMH DDI
Other Formats
  • JSON
  • BIBTEX

Confirm


Powered by WEKO3


Powered by WEKO3