{"_buckets": {"deposit": "3596666d-e351-4f20-8be9-c2b138e0819c"}, "_deposit": {"id": "4925", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4925"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4925", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Audio Fingerprinting based on Wavelet Spectral Entropy", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Audio-Fingerprints (AFPs) are essentialcharacteristics of digital audio streams used to scorethe perceptual similarity between audio signals.Audio-fingerprinting systems extract features fromthe signal normally on a frame by frame basis. Inthis paper, a robust audio-fingerprint (AFP)approach is developed based on spectral entropy inwavelet domain. To extract the fingerprints of asong, Shannon’s entropy is determined from thespectral coefficients of each one of the first 24critical bands according to the Bark scale. Theperformance of this AFP system is evaluated on amusic database containing various genres. Therobustness of the system is validated throughdegraded music signals in 4 different ways: whitenoise addition, lossy compression, lowpass filter andresampling."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value": []}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2019-07-12"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "psc2010paper (116).pdf", "filesize": [{"value": "47 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 47000.0, "url": {"url": "https://meral.edu.mm/record/4925/files/psc2010paper (116).pdf"}, "version_id": "5b98e720-8197-4a42-ab11-d9d72aeb96a2"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Fifth Local Conference on Parallel and Soft Computing", "subitem_pages": "", "subitem_volume": ""}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "", "subitem_c_date": "", "subitem_conference_title": "", "subitem_part": "", "subitem_place": "", "subitem_session": "", "subitem_website": ""}]}, "item_1583103211336": {"attribute_name": "Books/reports/chapters", "attribute_value_mlt": [{"subitem_book_title": "", "subitem_isbn": "", "subitem_pages": "", "subitem_place": "", "subitem_publisher": ""}]}, "item_1583103233624": {"attribute_name": "Thesis/dissertations", "attribute_value_mlt": [{"subitem_awarding_university": "", "subitem_supervisor(s)": [{"subitem_supervisor": ""}]}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Linn, Kyi Chan Nyein"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Article"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2010-12-16"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/827"}, "item_title": "Audio Fingerprinting based on Wavelet Spectral Entropy", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004925", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-12"}, "publish_date": "2019-07-12", "publish_status": "0", "recid": "4925", "relation": {}, "relation_version_is_last": true, "title": ["Audio Fingerprinting based on Wavelet Spectral Entropy"], "weko_shared_id": -1}