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M. Zanoni, F. Setragno, F. Antonacci, A. Sarti, G. Fazekas, and MA. B.. Sandler, "Training-Based Semantic Descriptors Modeling for Violin Quality Sound Characterization," Paper 9353, (2015 May.). doi: M. Zanoni, F. Setragno, F. Antonacci, A. Sarti, G. Fazekas, and MA. B.. Sandler, "Training-Based Semantic Descriptors Modeling for Violin Quality Sound Characterization," Paper 9353, (2015 May.). doi:
Abstract: Violin makers and musicians describe the timbral qualities of violins using semantic terms coming from natural language. In this study we use regression techniques of machine intelligence and audio features to model in a training-based fashion a set of high-level (semantic) descriptors for the automatic annotation of musical instruments. The most relevant semantic descriptors are collected through interviews to violin makers. These descriptors are then correlated with objective features extracted from a set of violins from the historical and contemporary collections of the Museo del Violino and of the International School of Luthiery both in Cremona. As sound description can vary throughout a performance, our approach also enables the modeling of time-varying (evolutive) semantic annotations.

@article{zanoni2015training-based,
author={zanoni, massimiliano and setragno, francesco and antonacci, fabio and sarti, augusto and fazekas, györgy and sandler, mark b.},
journal={journal of the audio engineering society},
title={training-based semantic descriptors modeling for violin quality sound characterization},
year={2015},
volume={},
number={},
pages={},
doi={},
month={may},} @article{zanoni2015training-based,
author={zanoni, massimiliano and setragno, francesco and antonacci, fabio and sarti, augusto and fazekas, györgy and sandler, mark b.},
journal={journal of the audio engineering society},
title={training-based semantic descriptors modeling for violin quality sound characterization},
year={2015},
volume={},
number={},
pages={},
doi={},
month={may},
abstract={violin makers and musicians describe the timbral qualities of violins using semantic terms coming from natural language. in this study we use regression techniques of machine intelligence and audio features to model in a training-based fashion a set of high-level (semantic) descriptors for the automatic annotation of musical instruments. the most relevant semantic descriptors are collected through interviews to violin makers. these descriptors are then correlated with objective features extracted from a set of violins from the historical and contemporary collections of the museo del violino and of the international school of luthiery both in cremona. as sound description can vary throughout a performance, our approach also enables the modeling of time-varying (evolutive) semantic annotations.},}

TY - paper
TI - Training-Based Semantic Descriptors Modeling for Violin Quality Sound Characterization
SP - EP -
AU - Zanoni, Massimiliano
AU - Setragno, Francesco
AU - Antonacci, Fabio
AU - Sarti, Augusto
AU - Fazekas, György
AU - Sandler, Mark B.
PY - 2015
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2015 TY - paper
TI - Training-Based Semantic Descriptors Modeling for Violin Quality Sound Characterization
SP - EP -
AU - Zanoni, Massimiliano
AU - Setragno, Francesco
AU - Antonacci, Fabio
AU - Sarti, Augusto
AU - Fazekas, György
AU - Sandler, Mark B.
PY - 2015
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2015
AB - Violin makers and musicians describe the timbral qualities of violins using semantic terms coming from natural language. In this study we use regression techniques of machine intelligence and audio features to model in a training-based fashion a set of high-level (semantic) descriptors for the automatic annotation of musical instruments. The most relevant semantic descriptors are collected through interviews to violin makers. These descriptors are then correlated with objective features extracted from a set of violins from the historical and contemporary collections of the Museo del Violino and of the International School of Luthiery both in Cremona. As sound description can vary throughout a performance, our approach also enables the modeling of time-varying (evolutive) semantic annotations.