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
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 outper
formed classical statistical methods.
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