2011
Iñesta, J. M.; Pérez-Sancho, C.; Hontanilla, M.
Composer Recognition using Language Models Proceedings Article
In: Proc. of Signal Processing, Pattern Recognition, and Applications (SPPRA 2011), pp. 76-83, ACTA Press, Innsbruck, Austria, 2011, ISBN: 978-0-88986-865-6.
Abstract | BibTeX | Tags: DRIMS, UA-CPS
@inproceedings{k261,
title = {Composer Recognition using Language Models},
author = {J. M. Iñesta and C. Pérez-Sancho and M. Hontanilla},
isbn = {978-0-88986-865-6},
year = {2011},
date = {2011-02-01},
urldate = {2011-02-01},
booktitle = {Proc. of Signal Processing, Pattern Recognition, and Applications (SPPRA 2011)},
pages = {76-83},
publisher = {ACTA Press},
address = {Innsbruck, Austria},
abstract = {In this paper we present an application of language modeling
techniques using n-grams to an authorship attribution
task. An stylometric study has been conducted on a pair
of datasets of baroque and classical composers, with which
other authors performed previously a similar study using a
set of musicological features and pattern recognition techniques.
In this paper, a simple general-purpose encoding
method has been used, in conjunction with language modeling
to explore the same problem. The results show that
this simpler method can lead to the same conclusions than
other more sophisticated methods, even traditional musicological
studies, without the need of advanced musicological
knowledge for processing the scores.},
keywords = {DRIMS, UA-CPS},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we present an application of language modeling
techniques using n-grams to an authorship attribution
task. An stylometric study has been conducted on a pair
of datasets of baroque and classical composers, with which
other authors performed previously a similar study using a
set of musicological features and pattern recognition techniques.
In this paper, a simple general-purpose encoding
method has been used, in conjunction with language modeling
to explore the same problem. The results show that
this simpler method can lead to the same conclusions than
other more sophisticated methods, even traditional musicological
studies, without the need of advanced musicological
knowledge for processing the scores.
2011
Iñesta, J. M.; Pérez-Sancho, C.; Hontanilla, M.
Composer Recognition using Language Models Proceedings Article
In: Proc. of Signal Processing, Pattern Recognition, and Applications (SPPRA 2011), pp. 76-83, ACTA Press, Innsbruck, Austria, 2011, ISBN: 978-0-88986-865-6.
Abstract | BibTeX | Tags: DRIMS, UA-CPS
@inproceedings{k261,
title = {Composer Recognition using Language Models},
author = {J. M. Iñesta and C. Pérez-Sancho and M. Hontanilla},
isbn = {978-0-88986-865-6},
year = {2011},
date = {2011-02-01},
urldate = {2011-02-01},
booktitle = {Proc. of Signal Processing, Pattern Recognition, and Applications (SPPRA 2011)},
pages = {76-83},
publisher = {ACTA Press},
address = {Innsbruck, Austria},
abstract = {In this paper we present an application of language modeling
techniques using n-grams to an authorship attribution
task. An stylometric study has been conducted on a pair
of datasets of baroque and classical composers, with which
other authors performed previously a similar study using a
set of musicological features and pattern recognition techniques.
In this paper, a simple general-purpose encoding
method has been used, in conjunction with language modeling
to explore the same problem. The results show that
this simpler method can lead to the same conclusions than
other more sophisticated methods, even traditional musicological
studies, without the need of advanced musicological
knowledge for processing the scores.},
keywords = {DRIMS, UA-CPS},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we present an application of language modeling
techniques using n-grams to an authorship attribution
task. An stylometric study has been conducted on a pair
of datasets of baroque and classical composers, with which
other authors performed previously a similar study using a
set of musicological features and pattern recognition techniques.
In this paper, a simple general-purpose encoding
method has been used, in conjunction with language modeling
to explore the same problem. The results show that
this simpler method can lead to the same conclusions than
other more sophisticated methods, even traditional musicological
studies, without the need of advanced musicological
knowledge for processing the scores.
techniques using n-grams to an authorship attribution
task. An stylometric study has been conducted on a pair
of datasets of baroque and classical composers, with which
other authors performed previously a similar study using a
set of musicological features and pattern recognition techniques.
In this paper, a simple general-purpose encoding
method has been used, in conjunction with language modeling
to explore the same problem. The results show that
this simpler method can lead to the same conclusions than
other more sophisticated methods, even traditional musicological
studies, without the need of advanced musicological
knowledge for processing the scores.