dc.contributor.author | Neocleous, Andreas C. | en |
dc.contributor.author | Petkov, N. | en |
dc.contributor.author | Schizas, Christos N. | en |
dc.contributor.editor | Madani K. | en |
dc.contributor.editor | Filipe J. | en |
dc.contributor.editor | Filipe J. | en |
dc.creator | Neocleous, Andreas C. | en |
dc.creator | Petkov, N. | en |
dc.creator | Schizas, Christos N. | en |
dc.date.accessioned | 2019-11-13T10:41:24Z | |
dc.date.available | 2019-11-13T10:41:24Z | |
dc.date.issued | 2014 | |
dc.identifier.isbn | 978-989-758-054-3 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/54580 | |
dc.description.abstract | Two different systems are introduced, that perform automated audio annotation and segmentation of Cypriot folk songs into meaningful musical information. The first system consists of three artificial neural networks (ANNs) using timbre low-level features. The output of the three networks is classifying an unknown song as "monophonic" or "polyphonic". The second system employs one ANN using the same feature set. This system takes as input a polyphonic song and it identifies the boundaries of the instrumental and vocal parts. For the classification of the "monophonic - polyphonic", a precision of 0.88 and a recall of 0.78 has been achieved. For the classification of the "vocal - instrumental" a precision of 0.85 and recall of 0.83 has been achieved. From the obtained results we concluded that the timbre low-level features were able to capture the characteristics of the audio signals. Also, that the specific ANN structures were suitable for the specific classification problem and outperformed classical statistical methods. | en |
dc.publisher | INSTICC Press | en |
dc.source | NCTA 2014 - Proceedings of the International Conference on Neural Computation Theory and Applications | en |
dc.source | 6th International Conference on Neural Computation Theory and Applications, NCTA 2014, Part of the 6th International Joint Conference on Computational Intelligence, IJCCI 2014 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908883988&partnerID=40&md5=1c6f8fd2bb17486beb027e7f32f769d0 | |
dc.subject | Artificial intelligence | en |
dc.subject | Neural networks | en |
dc.subject | Computation theory | en |
dc.subject | Audio systems | en |
dc.subject | Audio signal | en |
dc.subject | Computational intelligence | en |
dc.subject | Automated segmentation | en |
dc.subject | Audio acoustics | en |
dc.subject | Audio thumbnailing | en |
dc.subject | First systems | en |
dc.subject | Low-level features | en |
dc.subject | Musical information | en |
dc.subject | Polyphonic songs | en |
dc.subject | Singal processing | en |
dc.subject | Three networks | en |
dc.title | Automated segmentation of folk songs using artificial neural networks | en |
dc.type | info:eu-repo/semantics/conferenceObject | |
dc.description.startingpage | 144 | |
dc.description.endingpage | 151 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Conference Object | en |
dc.description.notes | <p>Sponsors: Institute for Systems and Technologies of Information, Control and Communication (INSTICC) | en |
dc.description.notes | International Federation of Automatic Control (IFAC) | en |
dc.description.notes | Conference code: 114694</p> | en |
dc.contributor.orcid | Schizas, Christos N. [0000-0001-6548-4980] | |
dc.gnosis.orcid | 0000-0001-6548-4980 | |