Automated segmentation of folk songs using artificial neural networks
Date
2014ISBN
978-989-758-054-3Publisher
INSTICC PressSource
NCTA 2014 - Proceedings of the International Conference on Neural Computation Theory and Applications6th International Conference on Neural Computation Theory and Applications, NCTA 2014, Part of the 6th International Joint Conference on Computational Intelligence, IJCCI 2014
Pages
144-151Google Scholar check
Keyword(s):
Metadata
Show full item recordAbstract
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.