{"_buckets": {"deposit": "a39eca82-ea5b-4392-a65c-c1bc7126f6cf"}, "_deposit": {"created_by": 3, "id": "7860", "owners": [3], "pid": {"revision_id": 0, "type": "depid", "value": "7860"}, "status": "published"}, "_oai": {"id": "oai:kyutech.repo.nii.ac.jp:00007860", "sets": ["9"]}, "author_link": ["402", "34530", "34536", "34526", "34531", "34533", "34527", "34532", "34537", "15968"], "item_21_alternative_title_18": {"attribute_name": "その他のタイトル", "attribute_value_mlt": [{"subitem_alternative_title": "Automatic Classification of Respiratory Sounds using HPSS"}]}, "item_21_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2021-07-06", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "2", "bibliographicPageEnd": "100", "bibliographicPageStart": "95", "bibliographicVolumeNumber": "38", "bibliographic_titles": [{"bibliographic_title": "医用画像情報学会雑誌"}]}]}, "item_21_description_4": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "Respiratory disease is a serious illness that accounts for three of the top ten causes of death in the world, and approximately eight million people died worldwide each year. Early detection and early treatment are important for the prevention of illness due to these diseases. Currently, auscultation is performed for the diagnosis of respiratory diseases, however there is a problem that quantitative diagnosis is difficult. Therefore, in this paper, we propose a new automatic classification method of respiratory sounds to support the diagnosis of respiratory diseases on auscultation. In the proposed method, respiratory sound data is converted into a spectrogram image by applying the short-time Fourier transform. Then, we apply HPSS (Harmonic/Percussive Sound Separation) algorithm to the respiratory sound spectrogram to separate it into a harmonic spectrogram and a percussive spectrogram. The three generated spectrograms are used for classification of respiratory sounds by CNN (Convolutional Neural Network) and SVM (Support Vector Machine) classifiers. Our proposed method obtained superior classification performance compared to the case without applying HPSS and satisfactory results are obtained.", "subitem_description_type": "Abstract"}]}, "item_21_description_60": {"attribute_name": "資源タイプ", "attribute_value_mlt": [{"subitem_description": "Journal Article", "subitem_description_type": "Other"}]}, "item_21_full_name_3": {"attribute_name": "著者別名", "attribute_value_mlt": [{"nameIdentifiers": [{"nameIdentifier": "34532", "nameIdentifierScheme": "WEKO"}], "names": [{"name": "Marubashi,  Yuki"}]}, {"nameIdentifiers": [{"nameIdentifier": "34533", "nameIdentifierScheme": "WEKO"}], "names": [{"name": "Asatani,  Naoki"}]}, {"nameIdentifiers": [{"nameIdentifier": "15968", "nameIdentifierScheme": "WEKO"}, {"nameIdentifier": "40742466", "nameIdentifierScheme": "e-Rad", "nameIdentifierURI": "https://nrid.nii.ac.jp/ja/nrid/1000040742466/"}, {"nameIdentifier": "57209823396", "nameIdentifierScheme": "Scopus著者ID", "nameIdentifierURI": 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{"subitem_text_value": "九州工業大学"}, {"subitem_text_value": "山口大学"}, {"subitem_text_value": "大阪大学"}]}, "item_21_text_63": {"attribute_name": "連携ID", "attribute_value_mlt": [{"subitem_text_value": "10838"}]}, "item_21_version_type_58": {"attribute_name": "著者版フラグ", "attribute_value_mlt": [{"subitem_version_resource": "http://purl.org/coar/version/c_970fb48d4fbd8a85", "subitem_version_type": "VoR"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "丸橋,  優生"}], "nameIdentifiers": [{"nameIdentifier": "34526", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "浅谷,  尚希"}], "nameIdentifiers": [{"nameIdentifier": "34527", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "陸, 慧敏"}], "nameIdentifiers": [{"nameIdentifier": "15968", "nameIdentifierScheme": "WEKO"}, {"nameIdentifier": "40742466", "nameIdentifierScheme": "e-Rad", "nameIdentifierURI": 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