{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T22:05:29Z","timestamp":1743113129862,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811980688"},{"type":"electronic","value":"9789811980695"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-19-8069-5_49","type":"book-chapter","created":{"date-parts":[[2022,11,19]],"date-time":"2022-11-19T10:07:42Z","timestamp":1668852462000},"page":"690-697","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Approach to\u00a0Hummed-tune and\u00a0Song Sequences Matching"],"prefix":"10.1007","author":[{"given":"Bao Loc","family":"Pham","sequence":"first","affiliation":[]},{"given":"Huong Hoang","family":"Luong","sequence":"additional","affiliation":[]},{"given":"Thien Phu","family":"Tran","sequence":"additional","affiliation":[]},{"given":"Hoang Phuc","family":"Ngo","sequence":"additional","affiliation":[]},{"given":"Hoang Vi","family":"Nguyen","sequence":"additional","affiliation":[]},{"given":"Thinh","family":"Nguyen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,20]]},"reference":[{"key":"49_CR1","unstructured":"Jiankang, D.: ArcFace: additive angular margin loss for deep face recognition. arXiv.Org, 23 January 2018. https:\/\/arxiv.org\/abs\/1801.07698"},{"key":"49_CR2","unstructured":"Jeff, J., et al.: Billion-0 Gpus. ArXiv.org, 28 February 2017. https:\/\/arxiv.org\/abs\/1702.08734"},{"key":"49_CR3","unstructured":"Kaiming, H., et al.: Deep residual learning for image recognition. ArXiv.org. 10 December 2015. https:\/\/arxiv.org\/abs\/1512.03385"},{"key":"49_CR4","unstructured":"Karen, S., Zisserman, A.: Very deep convolutional networks for large-scale image recognition.\u2019 ArXiv.org. 10 April 2015. https:\/\/arxiv.org\/abs\/1409.1556"},{"key":"49_CR5","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009). https:\/\/ieeexplore.ieee.org\/document\/5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"49_CR6","unstructured":"Mark, S., et al.: MobileNetV2: inverted residuals and linear bottlenecks. ArXiv.org. 21 March 2019. https:\/\/arxiv.org\/abs\/1801.04381"},{"key":"49_CR7","unstructured":"Alex, K.: ImageNet classification with deep convolutional neural networks. In: Proceedings. Neurips.Cc (2012). https:\/\/proceedings.neurips.cc\/paper\/2012\/hash\/c399862d3b9d6b76c8436e924a68c45b-Abstract.html"},{"key":"49_CR8","unstructured":"Avery, W.: An industrial strength audio search algorithm. An Industrial Strength Audio Search Algorithm. https:\/\/www.researchgate.net\/publication\/220723446_An_Industrial_Strength_Audio_Search_Algorithm"},{"key":"49_CR9","unstructured":"Keunwoo, C., et al.: Automatic tagging using deep convolutional neural networks. ArXiv.org. 1 June 2016. https:\/\/arxiv.org\/abs\/1606.00298"},{"key":"49_CR10","unstructured":"Jongpil, L., et al.: Sample-level deep convolutional neural networks for music auto-tagging using raw waveforms. ArXiv.org, 22 May 2017. https:\/\/arxiv.org\/abs\/1703.01789"},{"key":"49_CR11","unstructured":"Jordi, P., Serra, X.: Musicnn: pre-trained convolutional neural networks for music audio tagging. ArXiv.org, 14 September 2019. https:\/\/arxiv.org\/abs\/1909.06654"},{"key":"49_CR12","doi-asserted-by":"crossref","unstructured":"Jiang, C., et al.: Similarity learning for cover song identification using cross-similarity matrices of multi-level deep sequences. IEEE Xplore, 15 May 2020. https:\/\/ieeexplore.ieee.org\/document\/9053257","DOI":"10.1109\/ICASSP40776.2020.9053257"},{"key":"49_CR13","unstructured":"Xiaoshuo, X., et al.: Key-invariant convolutional neural network toward efficient cover song identification. IEEE Xplore, 11 October 2018. https:\/\/ieeexplore.ieee.org\/document\/8486531"},{"key":"49_CR14","unstructured":"Zhesong, Y., et al.: Learning a representation for cover song identification using convolutional neural network. ArXiv.org, 1 November 2019. https:\/\/arxiv.org\/abs\/1911.00334"},{"key":"49_CR15","unstructured":"Dong, Y., et al.: Contrastive learning with positive-negative frame mask for music representation. ArXiv.org, 3 April 2022. https:\/\/arxiv.org\/abs\/2203.09129"},{"key":"49_CR16","doi-asserted-by":"crossref","unstructured":"Quynh Nhut, N., et al.: Movie recommender systems made through tag interpolation. In: Proceedings of the 4th International Conference on Machine Learning and Soft Computing. ACM Other Conferences, 1 January 2020. https:\/\/dl.acm.org\/doi\/10.1145\/3380688.3380712","DOI":"10.1145\/3380688.3380712"},{"key":"49_CR17","unstructured":"Hao Tuan, H., et al.: Automatic keywords-based classification of vietnamese texts. In: 2020 RIVF International Conference on Computing and Communication Technologies (RIVF), IEEE (2020)"},{"key":"49_CR18","unstructured":"Quynh Nhut, N., et al.: Movie recommender systems made through tag interpolation. In: Proceedings of the 4th International Conference on Machine Learning and Soft Computing (2020)"},{"key":"49_CR19","doi-asserted-by":"crossref","unstructured":"Nghia, D.-T., et al.: Genres and actors\/actresses as interpolated tags for improving movie recommender systems. Int. J. Adv. Comput. Sci. Appl. 11(2) (2020)","DOI":"10.14569\/IJACSA.2020.0110210"},{"key":"49_CR20","unstructured":"Zalo AI Challenge. https:\/\/challenge.zalo.ai\/"},{"key":"49_CR21","unstructured":"Vovanphuc. VOVANPHUC\/hum2song: Top 1 Zalo AI Challenge 2021 Task Hum to Song. GitHub. https:\/\/github.com\/vovanphuc\/hum2song"},{"key":"49_CR22","unstructured":"Krishna, K.: Song Stuck in Your Head? Just Hum to Search. Google, Google, 15 October 2020. https:\/\/blog.google\/products\/search\/hum-to-search\/"},{"key":"49_CR23","unstructured":"Shazam. https:\/\/www.shazam.com\/"}],"container-title":["Communications in Computer and Information Science","Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-8069-5_49","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T03:48:17Z","timestamp":1728445697000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-8069-5_49"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811980688","9789811980695"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-8069-5_49","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"20 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FDSE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Future Data and Security Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ho Chi Minh City","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"fdse2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/thefdse.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"170","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"12","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4 full papers from invited keynote speakers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}