2015
1.
Neocleous, Andreas; Azzopardi, George; Schizas, Christos N; Petkov, Nicolai
Filter-Based Approach for Ornamentation Detection and Recognition in Singing Folk Music Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: signal processing, time-series, trainable filters
@inproceedings{neocleous2015filter,
title = {Filter-Based Approach for Ornamentation Detection and Recognition in Singing Folk Music},
author = {Andreas Neocleous and George Azzopardi and Christos N Schizas and Nicolai Petkov},
doi = {10.1007/978-3-319-23192-1_47},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {International Conference on Computer Analysis of Images and Patterns},
pages = {558--569},
organization = {Springer International Publishing},
abstract = {Ornamentations in music play a significant role for the emotion whi1ch 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 one-dimension (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.},
keywords = {signal processing, time-series, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
Ornamentations in music play a significant role for the emotion whi1ch 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 one-dimension (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.
2014
2.
de Vries, Harm; Azzopardi, George; Knobbe, Arno; Koelewijn, Andre
Parametric nonlinear regression models for dike monitoring systems Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: predictive analysis, time-series
@inproceedings{deVries2014,
title = {Parametric nonlinear regression models for dike monitoring systems},
author = {Harm de Vries and George Azzopardi and Arno Knobbe and Andre Koelewijn},
doi = {https://doi.org/10.1007/978-3-319-12571-8_30},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {Advances in Intelligent Data Analysis, LNCS
},
volume = {8819},
number = {345-355},
publisher = {Springer},
abstract = {Dike monitoring is crucial for protection against flooding disasters, an especially important topic in low countries, such as the Netherlands where many regions are below sea level. Recently, there has been growing interest in extending traditional dike monitoring by means of a sensor network. This paper presents a case study of a set of pore pressure sensors installed in a sea dike in Boston (UK), and which are continuously affected by water levels, the foremost influencing environmental factor. We estimate one-to-one relationships between a water height sensor and individual pore pressure sensors by parametric nonlinear regression models that are based on domain knowledge. We demonstrate the effectiveness of the proposed method by the high goodness of fits we obtain on real test data. Furthermore, we show how the proposed models can be used for the detection of anomalies.},
keywords = {predictive analysis, time-series},
pubstate = {published},
tppubtype = {inproceedings}
}
Dike monitoring is crucial for protection against flooding disasters, an especially important topic in low countries, such as the Netherlands where many regions are below sea level. Recently, there has been growing interest in extending traditional dike monitoring by means of a sensor network. This paper presents a case study of a set of pore pressure sensors installed in a sea dike in Boston (UK), and which are continuously affected by water levels, the foremost influencing environmental factor. We estimate one-to-one relationships between a water height sensor and individual pore pressure sensors by parametric nonlinear regression models that are based on domain knowledge. We demonstrate the effectiveness of the proposed method by the high goodness of fits we obtain on real test data. Furthermore, we show how the proposed models can be used for the detection of anomalies.