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Authors: Andreas Neocleous 1 ; Nicolai Petkov 2 and Christos N. Schizas 3

Affiliations: 1 University of Cyprus and University of Groningen, Cyprus ; 2 University of Groningen, Netherlands ; 3 University of Cyprus, Cyprus

ISBN: 978-989-758-054-3

Keyword(s): Audio Thumbnailing, Singal Processing, Computational Intelligence.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

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 outperf ormed classical statistical methods. (More)

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Paper citation in several formats:
Neocleous, A.; Petkov, N. and N. Schizas, C. (2014). Automated Segmentation of Folk Songs Using Artificial Neural Networks.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 144-151. DOI: 10.5220/0005049101440151

@conference{ncta14,
author={Andreas Neocleous. and Nicolai Petkov. and Christos N. Schizas.},
title={Automated Segmentation of Folk Songs Using Artificial Neural Networks},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},
year={2014},
pages={144-151},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005049101440151},
isbn={978-989-758-054-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)
TI - Automated Segmentation of Folk Songs Using Artificial Neural Networks
SN - 978-989-758-054-3
AU - Neocleous, A.
AU - Petkov, N.
AU - N. Schizas, C.
PY - 2014
SP - 144
EP - 151
DO - 10.5220/0005049101440151

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