Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/26587
Title: Filter-based approach for ornamentation detection and recognition in singing folk music
Authors: Azzopardi, George
Neocleous, Andreas
Schizas, Christos N.
Petkov, Nicolai
Keywords: Signal processing
Embellishment (Music)
Folk music -- Analysis, appreciation
Issue Date: 2015
Publisher: Springer, Cham
Citation: Neocleous, A., Azzopardi, G., Schizas, C. N., & Petkov, N. (2015). Filter-based approach for ornamentation detection and recognition in singing folk music. In G. Azzopardi, & N. Petkov (Eds.), Computer analysis of Images and patterns: 16th International Conference, CAIP 2015, Valletta, Malta, September 2-4, 2015 Proceedings, Part I, LNCS 9256 (pp. 558-569). Springer, Cham.
Abstract: Ornamentations in music play a significant role for the emotion which a performer or a composer aims to create. The automated identification of ornamentations enhances the understanding of music, which can be used as a feature for tasks such as performer identification or mood classification. Existing methods rely on a pre-processing step that performs note segmentation. We propose an alternative method by adapting the existing two-dimensional COSFIRE filter approach to onedimension (1D) for the automatic identification of ornamentations in monophonic folk songs. We construct a set of 1D COSFIRE filters that are selective for the 12 notes of the Western music theory. The response of a 1D COSFIRE filter is computed as the geometric mean of the differences between the fundamental frequency values in a local neighbourhood and the preferred values at the corresponding positions. We apply the proposed 1D COSFIRE filters to the pitch tracks of a song at every position along the entire signal, which in turn give response values in the range [0,1]. The 1D COSFIRE filters that we propose are effective to recognize meaningful musical information which can be transformed into symbolic representations and used for further analysis. We demonstrate the effectiveness of the proposed methodology in a new data set that we introduce, which comprises five monophonic Cypriot folk tunes consisting of 428 ornamentations. The proposed method is effective for the detection and recognition of ornamentations in singing folk music.
Description: This is a Conference paper presented by the authors at the CAiP 2015; 16th International Conference on Computer Analysis of Images and Patterns, held in Malta from the 2 to 4 September, 2015.
URI: http://www.springer.com/gp/book/9783319231914
https://www.um.edu.mt/library/oar//handle/123456789/26587
ISBN: 9783319231921
Appears in Collections:Scholarly Works - FacICTAI

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