{"_buckets": {"deposit": "f923d47a-bfca-4fd6-8643-84962353e62a"}, "_deposit": {"id": "3471", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "3471"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/3471", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Analytical Approach to MFCC Based Space-Saving Audio Fingerprinting System", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Audio fingerprinting is a smart technology toidentify music contents relevant to the query bycomparing the content-based hash (fingerprint) of thequery to known hashes in the fingerprint database. Fora million-song library, size of the fingerprint databaserestricts speedy and correct music identification. Inthis paper, we present a space-saving audiofingerprinting system based on Mel FrequencyCepstral Coefficients and analyze in detail. For anumber of cepstral coefficients with 12 as default, thesystem yields a 2712-bit fingerprint for a 3-sec audioclip, a significant reduction in storage compared to the8192-bit fingerprint of well-known Philips RobustHashing method. Experimental results also show thatthe more number of input value for cepstralcoefficients, the larger the fingerprint size. The choiceof Mel coefficients also affects the identificationperformance of the system: 8 to 12 coefficients givesthe best similarity rates while preserving robustness ofthe fingerprints to signal distortion such as pitchshifting."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "audio fingerprint"}, {"interim": "a million-song library"}, {"interim": "Mel Frequency Cepstral Coefficients"}, {"interim": "music identification"}, {"interim": "Philips Robust Hashing"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2019-07-23"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "ICCA 2019 Proceedings Book-pages-265-271.pdf", "filesize": [{"value": "823 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 823000.0, "url": {"url": "https://meral.edu.mm/record/3471/files/ICCA 2019 Proceedings Book-pages-265-271.pdf"}, "version_id": "6e4bc03f-cc6d-43b0-8b59-dca642117e55"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Seventeenth International Conference on Computer Applications(ICCA 2019)", "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": "Htun, Myo Thet"}]}]}, "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": "2019-02-27"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/1218"}, "item_title": "Analytical Approach to MFCC Based Space-Saving Audio Fingerprinting System", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000003471", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-23"}, "publish_date": "2019-07-23", "publish_status": "0", "recid": "3471", "relation": {}, "relation_version_is_last": true, "title": ["Analytical Approach to MFCC Based Space-Saving Audio Fingerprinting System"], "weko_shared_id": -1}