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P. Heydarian, L. Jones, and A. Seago, "Automatic Mode Estimation of Persian Musical Signals," Paper 8794, (2012 October.). doi: P. Heydarian, L. Jones, and A. Seago, "Automatic Mode Estimation of Persian Musical Signals," Paper 8794, (2012 October.). doi:
Abstract: Musical mode is central to maqamic musical traditions that span from Western China to Southern Europe. A mode usually represents the scale and is to some extent an indication of the emotional content of a piece. Knowledge of the mode is useful in searching multicultural archives of maqamic musical signals. Thus, the modal information is worth inclusion in metadata of a file. An automatic mode classification algorithm will have potential applications in music recommendation and play list generation, where the pieces can be ordered based on a perceptually accepted criterion such as the mode. It has the possibility of being used as a framework for music composition and synthesis. This paper presents an algorithm for classification of Persian audio musical signals, based on a generative approach, i.e., Gaussian Mixture Models (GMM), where chroma is used as the feature. The results will be compared with a chroma-based method with a Manhattan distance measure that was previously developed by ourselves.

@article{heydarian2012automatic,
author={heydarian, peyman and jones, lewis and seago, allan},
journal={journal of the audio engineering society},
title={automatic mode estimation of persian musical signals},
year={2012},
volume={},
number={},
pages={},
doi={},
month={october},} @article{heydarian2012automatic,
author={heydarian, peyman and jones, lewis and seago, allan},
journal={journal of the audio engineering society},
title={automatic mode estimation of persian musical signals},
year={2012},
volume={},
number={},
pages={},
doi={},
month={october},
abstract={musical mode is central to maqamic musical traditions that span from western china to southern europe. a mode usually represents the scale and is to some extent an indication of the emotional content of a piece. knowledge of the mode is useful in searching multicultural archives of maqamic musical signals. thus, the modal information is worth inclusion in metadata of a file. an automatic mode classification algorithm will have potential applications in music recommendation and play list generation, where the pieces can be ordered based on a perceptually accepted criterion such as the mode. it has the possibility of being used as a framework for music composition and synthesis. this paper presents an algorithm for classification of persian audio musical signals, based on a generative approach, i.e., gaussian mixture models (gmm), where chroma is used as the feature. the results will be compared with a chroma-based method with a manhattan distance measure that was previously developed by ourselves.},}

TY - paper
TI - Automatic Mode Estimation of Persian Musical Signals
SP - EP -
AU - Heydarian, Peyman
AU - Jones, Lewis
AU - Seago, Allan
PY - 2012
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2012 TY - paper
TI - Automatic Mode Estimation of Persian Musical Signals
SP - EP -
AU - Heydarian, Peyman
AU - Jones, Lewis
AU - Seago, Allan
PY - 2012
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2012
AB - Musical mode is central to maqamic musical traditions that span from Western China to Southern Europe. A mode usually represents the scale and is to some extent an indication of the emotional content of a piece. Knowledge of the mode is useful in searching multicultural archives of maqamic musical signals. Thus, the modal information is worth inclusion in metadata of a file. An automatic mode classification algorithm will have potential applications in music recommendation and play list generation, where the pieces can be ordered based on a perceptually accepted criterion such as the mode. It has the possibility of being used as a framework for music composition and synthesis. This paper presents an algorithm for classification of Persian audio musical signals, based on a generative approach, i.e., Gaussian Mixture Models (GMM), where chroma is used as the feature. The results will be compared with a chroma-based method with a Manhattan distance measure that was previously developed by ourselves.