2010
Pérez-Sancho, C.; Rizo, D.; Iñesta, J. M.; León, P. J. Ponce; Kersten, S.; Ramírez, R.
Genre classification of music by tonal harmony Journal Article
In: Intelligent Data Analysis, vol. 14, no. 5, pp. 533-545, 2010, ISSN: 1088-467X.
Abstract | BibTeX | Tags: Acc. Int. E-A, DRIMS, PROSEMUS
@article{k232,
title = {Genre classification of music by tonal harmony},
author = {C. Pérez-Sancho and D. Rizo and J. M. Iñesta and P. J. Ponce León and S. Kersten and R. Ramírez},
issn = {1088-467X},
year = {2010},
date = {2010-09-01},
urldate = {2010-09-01},
journal = {Intelligent Data Analysis},
volume = {14},
number = {5},
pages = {533-545},
abstract = {In this paper we present a genre classification framework for audio music based on a symbolic classification system. Audio signals are transformed into a symbolic representation of harmony using a chord transcription algorithm, based on the computation of harmonic pitch class profiles. Then, language models built from a ground truth of chord progressions for each genre are used to perform classification. We show that chord progressions are a suitable feature to represent musical genre, as they capture the harmonic rules relevant in each musical period or style. Finally, results using both pure symbolic information and chords transcribed from audio-from-MIDI are compared, in order to evaluate the effects of the transcription process in this task.},
keywords = {Acc. Int. E-A, DRIMS, PROSEMUS},
pubstate = {published},
tppubtype = {article}
}
In this paper we present a genre classification framework for audio music based on a symbolic classification system. Audio signals are transformed into a symbolic representation of harmony using a chord transcription algorithm, based on the computation of harmonic pitch class profiles. Then, language models built from a ground truth of chord progressions for each genre are used to perform classification. We show that chord progressions are a suitable feature to represent musical genre, as they capture the harmonic rules relevant in each musical period or style. Finally, results using both pure symbolic information and chords transcribed from audio-from-MIDI are compared, in order to evaluate the effects of the transcription process in this task.2009
Rizo, D.; Lemström, K.; Iñesta, J. M.
Ensemble of state-of-the-art methods for polyphonic music comparison Proceedings Article
In: Rauber, A.; Orio, N.; Rizo, D. (Ed.): Proceedings of the Workshop on Exploring Musical Information Spaces, ECDL 2009, pp. 46–51, Corfu, Greece, 2009, ISBN: 978-84-692-6082-1.
Abstract | Links | BibTeX | Tags: PROSEMUS
@inproceedings{k233,
title = {Ensemble of state-of-the-art methods for polyphonic music comparison},
author = {D. Rizo and K. Lemström and J. M. Iñesta},
editor = {A. Rauber and N. Orio and D. Rizo},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/233/wemis2009.pdf},
isbn = {978-84-692-6082-1},
year = {2009},
date = {2009-10-01},
urldate = {2009-10-01},
booktitle = {Proceedings of the Workshop on Exploring Musical Information Spaces, ECDL 2009},
pages = {46--51},
address = {Corfu, Greece},
abstract = {Content-based music comparison is a task where no musical similarity measure can perform well in all possible cases. In this paper we will show that a careful combination of different similarity measures in an ensemble measure, will behave more robust than any of the included individual measures when applied as stand-alone measures. For the experiments we have used five state-of-the-art polyphonic similarity measures and three different corpora of polyphonic music.},
keywords = {PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
Content-based music comparison is a task where no musical similarity measure can perform well in all possible cases. In this paper we will show that a careful combination of different similarity measures in an ensemble measure, will behave more robust than any of the included individual measures when applied as stand-alone measures. For the experiments we have used five state-of-the-art polyphonic similarity measures and three different corpora of polyphonic music. Iñesta, J. M.; Pérez-García, T.; Rizo, D.
metamidi: a tool for automatic metadata extraction from MIDI files Proceedings Article
In: Rauber, A.; Orio, N.; Rizo, D. (Ed.): Proceedings of the Workshop on Exploring Musical Information Spaces, ECDL 2009, pp. 36–40, Corfu, Greece, 2009, ISBN: 978-84-692-6082-1.
Abstract | Links | BibTeX | Tags: PROSEMUS
@inproceedings{k234,
title = {metamidi: a tool for automatic metadata extraction from MIDI files},
author = {J. M. Iñesta and T. Pérez-García and D. Rizo},
editor = {A. Rauber and N. Orio and D. Rizo},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/234/metamidi.pdf},
isbn = {978-84-692-6082-1},
year = {2009},
date = {2009-10-01},
urldate = {2009-10-01},
booktitle = {Proceedings of the Workshop on Exploring Musical Information Spaces, ECDL 2009},
pages = {36--40},
address = {Corfu, Greece},
abstract = {The increasing availability of on-line music has motivated a growing interest for organizing, commercializing, and delivering this kind of multimedia content. For it, the use of metadata is of utmost importance. Metadata permit organization, indexing, and retrieval of music contents. They are, therefore, a subject of research both from the design and automatic extraction approaches. The present work focuses on this second issue, providing an open source tool for metadata extraction from standard MIDI files. The tool is presented, the utilized metadata are explained, and some applications and experiments are described as examples of its capabilities.},
keywords = {PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
The increasing availability of on-line music has motivated a growing interest for organizing, commercializing, and delivering this kind of multimedia content. For it, the use of metadata is of utmost importance. Metadata permit organization, indexing, and retrieval of music contents. They are, therefore, a subject of research both from the design and automatic extraction approaches. The present work focuses on this second issue, providing an open source tool for metadata extraction from standard MIDI files. The tool is presented, the utilized metadata are explained, and some applications and experiments are described as examples of its capabilities. Pérez-Sancho, C.
Stochastic Language Models for Music Information Retrieval PhD Thesis
2009.
Abstract | Links | BibTeX | Tags: Acc. Int. E-A, PROSEMUS
@phdthesis{k240,
title = {Stochastic Language Models for Music Information Retrieval},
author = {C. Pérez-Sancho},
editor = {Jorge Calera Rubio José M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/240/phdthesis_cperez.pdf},
year = {2009},
date = {2009-07-01},
address = {Alicante, Spain},
organization = {Universidad de Alicante},
abstract = {Music Information Retrieval (MIR) is an interdisciplinary research area that aims at providing solutions to most problems related to the access to multimedia databases, in particular those with musical content, either in symbolic (MIDI) or audio format. An especially relevant problem is the automatic organization and indexation of data, since carrying out these tasks by hand would require an overwhelming effort for most people and institutions.
One of the most relevant features that can be obtained from a song in order to perform its automatic organization is the musical style, since it is one of the most popular fields used by people when accessing musical databases and catalogs. In this thesis it has been studied to what extent the musical style of a piece can be determined using just the information contained in its score, by applying pattern recognition techniques on melodic and harmonic sequences obtained from musical scores.},
keywords = {Acc. Int. E-A, PROSEMUS},
pubstate = {published},
tppubtype = {phdthesis}
}
Music Information Retrieval (MIR) is an interdisciplinary research area that aims at providing solutions to most problems related to the access to multimedia databases, in particular those with musical content, either in symbolic (MIDI) or audio format. An especially relevant problem is the automatic organization and indexation of data, since carrying out these tasks by hand would require an overwhelming effort for most people and institutions.
One of the most relevant features that can be obtained from a song in order to perform its automatic organization is the musical style, since it is one of the most popular fields used by people when accessing musical databases and catalogs. In this thesis it has been studied to what extent the musical style of a piece can be determined using just the information contained in its score, by applying pattern recognition techniques on melodic and harmonic sequences obtained from musical scores. Pérez-Sancho, C.; Rizo, D.; Iñesta, J. M.
Genre classification using chords and stochastic language models Journal Article
In: Connection Science, vol. 21, no. 2, pp. 145-159, 2009, ISSN: 0954-0091.
Abstract | BibTeX | Tags: Acc. Int. E-A, MIPRCV, PROSEMUS
@article{k227,
title = {Genre classification using chords and stochastic language models},
author = {C. Pérez-Sancho and D. Rizo and J. M. Iñesta},
issn = {0954-0091},
year = {2009},
date = {2009-05-01},
urldate = {2009-05-01},
journal = {Connection Science},
volume = {21},
number = {2},
pages = {145-159},
abstract = {Music genre meta-data is of paramount importance for the organisation of music repositories. People use genre in a natural way when entering a music store or looking into music collections. Automatic genre classification has become a popular topic in music information retrieval research both, with digital audio and symbolic data. This work focuses on the symbolic approach, bringing to music cognition some technologies, like the stochastic language models, already successfully applied to text categorisation. The representation chosen here is to model chord progressions as n-grams and strings and then apply perplexity and naiumlve Bayes classifiers, respectively, in order to assess how often those structures are found in the target genres. Some genres and sub-genres among popular, jazz, and academic music have been considered, trying to investigate how far can we reach using harmonic information with these models. The results at different levels of the genre hierarchy for the techniques employed are presented and discussed.},
keywords = {Acc. Int. E-A, MIPRCV, PROSEMUS},
pubstate = {published},
tppubtype = {article}
}
Music genre meta-data is of paramount importance for the organisation of music repositories. People use genre in a natural way when entering a music store or looking into music collections. Automatic genre classification has become a popular topic in music information retrieval research both, with digital audio and symbolic data. This work focuses on the symbolic approach, bringing to music cognition some technologies, like the stochastic language models, already successfully applied to text categorisation. The representation chosen here is to model chord progressions as n-grams and strings and then apply perplexity and naiumlve Bayes classifiers, respectively, in order to assess how often those structures are found in the target genres. Some genres and sub-genres among popular, jazz, and academic music have been considered, trying to investigate how far can we reach using harmonic information with these models. The results at different levels of the genre hierarchy for the techniques employed are presented and discussed. Rizo, D.; Lemström, K.; Iñesta, J. M.
Tree representation in combined polyphonic music comparison Journal Article
In: Computer Music Modeling and Retrieval. Genesis of Meaning in Sound and Music. Lecture Notes in Computer Science, vol. 5493, pp. 177–195, 2009, ISSN: 978-3-642-02517-4.
Abstract | BibTeX | Tags: PROSEMUS
@article{k229,
title = {Tree representation in combined polyphonic music comparison},
author = {D. Rizo and K. Lemström and J. M. Iñesta},
issn = {978-3-642-02517-4},
year = {2009},
date = {2009-01-01},
journal = {Computer Music Modeling and Retrieval. Genesis of Meaning in Sound and Music. Lecture Notes in Computer Science},
volume = {5493},
pages = {177--195},
abstract = {Identifying copies or different versions of a same musical work is a focal problem in maintaining large music databases. In this paper we introduce novel ideas and methods that are applicable to metered, symbolically encoded polyphonic music. We show how to represent and compare polyphonic music using a tree structure. Moreover, we put for trial various comparison methods and observe whether better comparison results can be obtained by combining distinct similarity measures. Our experiments show that the proposed representation is adequate for the task with good quality results and processing times, and when combined with other methods it becomes more robust against various types of music.},
keywords = {PROSEMUS},
pubstate = {published},
tppubtype = {article}
}
Identifying copies or different versions of a same musical work is a focal problem in maintaining large music databases. In this paper we introduce novel ideas and methods that are applicable to metered, symbolically encoded polyphonic music. We show how to represent and compare polyphonic music using a tree structure. Moreover, we put for trial various comparison methods and observe whether better comparison results can be obtained by combining distinct similarity measures. Our experiments show that the proposed representation is adequate for the task with good quality results and processing times, and when combined with other methods it becomes more robust against various types of music. Calera-Rubio, J.; Bernabeu, J. F.
A probabilistic approach to melodic similarity Proceedings Article
In: Proceedings of MML 2009, pp. 48-53, 2009.
Abstract | Links | BibTeX | Tags: ARFAI, DRIMS, MIPRCV, PROSEMUS, TIASA
@inproceedings{k231,
title = {A probabilistic approach to melodic similarity},
author = {J. Calera-Rubio and J. F. Bernabeu},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/231/mml2009Bernabeu.pdf},
year = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
booktitle = {Proceedings of MML 2009},
pages = {48-53},
abstract = {Melodic similarity is an important research topic in music information retrieval.
The representation of symbolic music by means of trees has proven to be suitable
in melodic similarity computation, because they are able to code rhythm in their
structure leaving only pitch representations as a degree of freedom for coding.
In order to compare trees, different edit distances have been previously used.
In this paper, stochastic k-testable tree-models, formerly used in other domains
like structured document compression or natural language processing, have been
used for computing a similarity measure between melody trees as a probability
and their performance has been compared to a classical tree edit distance.},
keywords = {ARFAI, DRIMS, MIPRCV, PROSEMUS, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
Melodic similarity is an important research topic in music information retrieval.
The representation of symbolic music by means of trees has proven to be suitable
in melodic similarity computation, because they are able to code rhythm in their
structure leaving only pitch representations as a degree of freedom for coding.
In order to compare trees, different edit distances have been previously used.
In this paper, stochastic k-testable tree-models, formerly used in other domains
like structured document compression or natural language processing, have been
used for computing a similarity measure between melody trees as a probability
and their performance has been compared to a classical tree edit distance. Pertusa, A.; Iñesta, J. M.
Note Onset Detection Using One Semitone Filter-Bank For MIREX 2009 Proceedings Article
In: MIREX 2009 - Music Information Retrieval Evaluation eXchange, MIREX Audio Onset Detection, Kobe, Japan., 2009.
Links | BibTeX | Tags: Acc. Int. E-A, PROSEMUS
@inproceedings{k238,
title = {Note Onset Detection Using One Semitone Filter-Bank For MIREX 2009},
author = {A. Pertusa and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/238/PI.pdf},
year = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
booktitle = {MIREX 2009 - Music Information Retrieval Evaluation eXchange, MIREX Audio Onset Detection},
address = {Kobe, Japan.},
keywords = {Acc. Int. E-A, PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
2008
León, P. J. Ponce; Iñesta, J. M.; Rizo, D.
Mining digital music score collections: melody extraction and genre recognition Book Chapter
In: Yin, Peng-Yeng (Ed.): Pattern Recognition, Chapter 25, pp. 559-590, IN-TECH, Vienna, Austria, 2008, ISBN: 978-3-902613-24-4.
Abstract | BibTeX | Tags: PROSEMUS
@inbook{k225,
title = {Mining digital music score collections: melody extraction and genre recognition},
author = {P. J. Ponce León and J. M. Iñesta and D. Rizo},
editor = {Peng-Yeng Yin},
isbn = {978-3-902613-24-4},
year = {2008},
date = {2008-11-01},
urldate = {2008-11-01},
booktitle = {Pattern Recognition},
pages = {559-590},
publisher = {IN-TECH},
address = {Vienna, Austria},
chapter = {25},
abstract = {In the field of computer music, pattern recognition algorithms are very
relevant for music information retrieval (MIR) applications. Two challenging
tasks in this area is the automatic recognition of musical genre and melody extraction, having a
number of applications like indexing and selecting musical databases.
One of the main references for music is its melody. In a practical environment of digital music score collections the information can be found in standard MIDI file format. Music is structured as a number of tracks in this file format, usually one of them containing the melodic line, while others tracks contain the accompaniment.
Finding that melody track is very useful for a number of applications, like speeding up melody
matching when searching in MIDI databases, extracting motifs for musicological analysis, building
music thumbnails or extracting melodic ringtones from MIDI files.
In the first part of this chapter,
musical content information is modeled by computing global statistical descriptors from track content.
These descriptors are the input to a random forest classifier
that assigns the probability of being a melodic line to each track. The
track with the highest probability is then selected as the one containing the
melodic line of the MIDI file. The first part of this chapter ends with a discussion on results obtained from a number of databases of different music styles.
The second part of the chapter deals with the problem of classifying such melodies in a collection of music genres. A slightly different approach is used for this task, first dividing a melody track in segments of fixed length. Statistical features are extracted for each segment and used to classify them as one of several genres.
The proposed methodology
is presented, covering the feature extraction, feature selection,
and genre classification stages. Different supervised classification
methods, like Bayesian classifier and nearest neighbors are applied. As a proof of concept, the performance of such algorithms against different description models and parameters is analyzed for two particular musical genres, like jazz and classical music.},
keywords = {PROSEMUS},
pubstate = {published},
tppubtype = {inbook}
}
In the field of computer music, pattern recognition algorithms are very
relevant for music information retrieval (MIR) applications. Two challenging
tasks in this area is the automatic recognition of musical genre and melody extraction, having a
number of applications like indexing and selecting musical databases.
One of the main references for music is its melody. In a practical environment of digital music score collections the information can be found in standard MIDI file format. Music is structured as a number of tracks in this file format, usually one of them containing the melodic line, while others tracks contain the accompaniment.
Finding that melody track is very useful for a number of applications, like speeding up melody
matching when searching in MIDI databases, extracting motifs for musicological analysis, building
music thumbnails or extracting melodic ringtones from MIDI files.
In the first part of this chapter,
musical content information is modeled by computing global statistical descriptors from track content.
These descriptors are the input to a random forest classifier
that assigns the probability of being a melodic line to each track. The
track with the highest probability is then selected as the one containing the
melodic line of the MIDI file. The first part of this chapter ends with a discussion on results obtained from a number of databases of different music styles.
The second part of the chapter deals with the problem of classifying such melodies in a collection of music genres. A slightly different approach is used for this task, first dividing a melody track in segments of fixed length. Statistical features are extracted for each segment and used to classify them as one of several genres.
The proposed methodology
is presented, covering the feature extraction, feature selection,
and genre classification stages. Different supervised classification
methods, like Bayesian classifier and nearest neighbors are applied. As a proof of concept, the performance of such algorithms against different description models and parameters is analyzed for two particular musical genres, like jazz and classical music. León, P. J. Ponce; Rizo, D.; Ramírez, R.; Iñesta, J. M.
Melody Characterization by a Genetic Fuzzy System Proceedings Article
In: Supper, Martin; Weinzierl, Stefan (Ed.): Proceedings of the 5th Sound and Music Computing Conference, pp. 15-23, Universitätsverlag der TU Berlin, 2008, ISBN: 978-3-7983-2094-9.
Abstract | Links | BibTeX | Tags: PROSEMUS
@inproceedings{k212,
title = {Melody Characterization by a Genetic Fuzzy System},
author = {P. J. Ponce León and D. Rizo and R. Ramírez and J. M. Iñesta},
editor = {Martin Supper and Stefan Weinzierl},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/212/smc-08.pdf},
isbn = {978-3-7983-2094-9},
year = {2008},
date = {2008-07-01},
urldate = {2008-07-01},
booktitle = {Proceedings of the 5th Sound and Music Computing Conference},
pages = {15-23},
publisher = {Universitätsverlag der TU Berlin},
abstract = {We present preliminary work on automatic human-readable melody
characterization. In order to obtain such a characterization, we
(1) extract a set of statistical descriptors from the tracks in a dataset of MIDI files,
(2) apply a rule induction algorithm to obtain a set of (crisp) classification
rules for melody track identification, and (3) automatically transform the
crisp rules into fuzzy rules by applying a genetic algorithm to generate the membership
functions for the rule attributes.
Some results are presented and discussed.},
keywords = {PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
We present preliminary work on automatic human-readable melody
characterization. In order to obtain such a characterization, we
(1) extract a set of statistical descriptors from the tracks in a dataset of MIDI files,
(2) apply a rule induction algorithm to obtain a set of (crisp) classification
rules for melody track identification, and (3) automatically transform the
crisp rules into fuzzy rules by applying a genetic algorithm to generate the membership
functions for the rule attributes.
Some results are presented and discussed. Lemström, K.; Rizo, D.
Tree structured and combined methods for comparing metered polyphonic music Proceedings Article
In: Proc. Computer Music Modeling and Retrieval 2008 (CMMR'08), pp. 263–278, Copenhagen, Denmark, 2008, ISBN: 978-87-7606-027-5.
@inproceedings{k206,
title = {Tree structured and combined methods for comparing metered polyphonic music},
author = {K. Lemström and D. Rizo},
isbn = {978-87-7606-027-5},
year = {2008},
date = {2008-05-01},
urldate = {2008-05-01},
booktitle = {Proc. Computer Music Modeling and Retrieval 2008 (CMMR'08)},
pages = {263--278},
address = {Copenhagen, Denmark},
keywords = {PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
Iñesta, J. M.; Pertusa, A.
Multiple Fundamental Frequency estimation using Gaussian smoothness Proceedings Article
In: Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP 2008, pp. 105-108, Las Vegas, USA, 2008, ISBN: 1-4244-1484-9.
Links | BibTeX | Tags: Acc. Int. E-A, MIPRCV, PROSEMUS
@inproceedings{k202,
title = {Multiple Fundamental Frequency estimation using Gaussian smoothness},
author = {J. M. Iñesta and A. Pertusa},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/202/2974_sent.pdf},
isbn = {1-4244-1484-9},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
booktitle = {Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP 2008},
pages = {105-108},
address = {Las Vegas, USA},
keywords = {Acc. Int. E-A, MIPRCV, PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
Ramírez, R.; Pérez, A.; Maestre, E.; Kersten, S.; Rizo, D.; Illescas, P. R.; Iñesta, J. M.
Modeling celtic violin expressive performance Proceedings Article
In: Proc. Int. Workshop on Machine Learning and Music, MML 2008, pp. 7-8, Helsinki, Finland, 2008.
@inproceedings{k208,
title = {Modeling celtic violin expressive performance},
author = {R. Ramírez and A. Pérez and E. Maestre and S. Kersten and D. Rizo and P. R. Illescas and J. M. Iñesta},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
booktitle = {Proc. Int. Workshop on Machine Learning and Music, MML 2008},
pages = {7-8},
address = {Helsinki, Finland},
keywords = {PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
Pérez-Sancho, C.; Rizo, D.; Kersten, S.; Ramírez, R.
Genre classification of music by tonal harmony Proceedings Article
In: Proc. Int. Workshop on Machine Learning and Music, MML 2008, pp. 21-22, Helsinki, Finland, 2008.
BibTeX | Tags: Acc. Int. E-A, MIPRCV, PROSEMUS
@inproceedings{k209,
title = {Genre classification of music by tonal harmony},
author = {C. Pérez-Sancho and D. Rizo and S. Kersten and R. Ramírez},
year = {2008},
date = {2008-01-01},
booktitle = {Proc. Int. Workshop on Machine Learning and Music, MML 2008},
pages = {21-22},
address = {Helsinki, Finland},
keywords = {Acc. Int. E-A, MIPRCV, PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
Rizo, D.; Illescas, P. R.
Learning to analyse tonal music Proceedings Article
In: Proc. Int. Workshop on Machine Learning and Music, MML 2008, pp. 25-26, Helsinki, Finland, 2008.
BibTeX | Tags: MIPRCV, PROSEMUS
@inproceedings{k210,
title = {Learning to analyse tonal music},
author = {D. Rizo and P. R. Illescas},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
booktitle = {Proc. Int. Workshop on Machine Learning and Music, MML 2008},
pages = {25-26},
address = {Helsinki, Finland},
keywords = {MIPRCV, PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
León, P. J. Ponce; Rizo, D.; Iñesta, J. M.
Melody characterization by a fuzzy rule system Proceedings Article
In: Proc. Int. Workshop on Machine Learning and Music, MML 2008, pp. 35-36, Helsinki, Finland, 2008.
@inproceedings{k211,
title = {Melody characterization by a fuzzy rule system},
author = {P. J. Ponce León and D. Rizo and J. M. Iñesta},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
booktitle = {Proc. Int. Workshop on Machine Learning and Music, MML 2008},
pages = {35-36},
address = {Helsinki, Finland},
keywords = {PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
Iñesta, J. M.; Heredia-Agoiz, J. L.; León, P. J. Ponce
A ground-truth experiment on melody genre recognition in absence of timbre Proceedings Article
In: Proc. of the 10th International Conference on Music Perception and Cognition (ICMPC10), pp. 758-761, Sapporo, Japan, 2008, ISBN: 978-4-9904208-0-2.
BibTeX | Tags: Acc. Int. E-A, PROSEMUS
@inproceedings{k215,
title = {A ground-truth experiment on melody genre recognition in absence of timbre},
author = {J. M. Iñesta and J. L. Heredia-Agoiz and P. J. Ponce León},
isbn = {978-4-9904208-0-2},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
booktitle = {Proc. of the 10th International Conference on Music Perception and Cognition (ICMPC10)},
pages = {758-761},
address = {Sapporo, Japan},
keywords = {Acc. Int. E-A, PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
Pérez-Sancho, C.; Rizo, D.; Iñesta, J. M.
Stochastic text models for music categorization Journal Article
In: Lecture Notes in Computer Science, vol. 5342, pp. 55-64, 2008.
Links | BibTeX | Tags: Acc. Int. E-A, MIPRCV, PROSEMUS
@article{k217,
title = {Stochastic text models for music categorization},
author = {C. Pérez-Sancho and D. Rizo and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/217/music-cat.pdf},
year = {2008},
date = {2008-01-01},
journal = {Lecture Notes in Computer Science},
volume = {5342},
pages = {55-64},
keywords = {Acc. Int. E-A, MIPRCV, PROSEMUS},
pubstate = {published},
tppubtype = {article}
}
Habrard, A.; Iñesta, J. M.; Rizo, D.; Sebban, M.
Melody recognition with learned edit distances Journal Article
In: Lecture Notes in Computer Science, vol. 5342, pp. 86-96, 2008.
Links | BibTeX | Tags: MIPRCV, PROSEMUS
@article{k218,
title = {Melody recognition with learned edit distances},
author = {A. Habrard and J. M. Iñesta and D. Rizo and M. Sebban},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/218/melody-rec.pdf},
year = {2008},
date = {2008-01-01},
journal = {Lecture Notes in Computer Science},
volume = {5342},
pages = {86-96},
keywords = {MIPRCV, PROSEMUS},
pubstate = {published},
tppubtype = {article}
}
Lidy, T.; Rauber, A.; Pertusa, A.; de León, P. J. Ponce; Iñesta, J. M.
MIREX 2008: Audio Music Classification Using A Combination Of Spectral, Timbral, Rhythmic, Temporal And Symbolic Features. Proceedings Article
In: MIREX 2008 - Music Information Retrieval Evaluation eXchange, MIREX Genre Classification Contest., Philadelphia, Pennsylvania, USA, 2008.
Abstract | Links | BibTeX | Tags: Acc. Int. E-A, PROSEMUS
@inproceedings{k236,
title = {MIREX 2008: Audio Music Classification Using A Combination Of Spectral, Timbral, Rhythmic, Temporal And Symbolic Features.},
author = {T. Lidy and A. Rauber and A. Pertusa and P. J. Ponce de León and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/236/abstract_mirex08_class.pdf},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
booktitle = {MIREX 2008 - Music Information Retrieval Evaluation eXchange, MIREX Genre Classification Contest.},
address = {Philadelphia, Pennsylvania, USA},
abstract = {The novel approach of combining audio and symbolic features
for music classification from audio enhanced previous
audio-only based results in MIREX 2007. We extended the
approach by including temporal audio features, enhancing
the polyphonic audio to MIDI transcription system and including
an extended set of symbolic features. Recent research
in music genre classification hints at a glass ceiling
being reached using timbral audio features.},
keywords = {Acc. Int. E-A, PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
The novel approach of combining audio and symbolic features
for music classification from audio enhanced previous
audio-only based results in MIREX 2007. We extended the
approach by including temporal audio features, enhancing
the polyphonic audio to MIDI transcription system and including
an extended set of symbolic features. Recent research
in music genre classification hints at a glass ceiling
being reached using timbral audio features. Pertusa, A.; Iñesta, J. M.
Multiple Fundamental Frequency Estimation Using Gaussian Smoothness And Short Context. Proceedings Article
In: MIREX 2008 - Music Information Retrieval Evaluation eXchange, MIREX Fundamental Frequency Estimation & Tracking Contest., Philadelphia, Pennsylvania, USA, 2008.
Links | BibTeX | Tags: Acc. Int. E-A, MIPRCV, PROSEMUS
@inproceedings{k237,
title = {Multiple Fundamental Frequency Estimation Using Gaussian Smoothness And Short Context.},
author = {A. Pertusa and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/237/F0_pertusa.pdf},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
booktitle = {MIREX 2008 - Music Information Retrieval Evaluation eXchange, MIREX Fundamental Frequency Estimation & Tracking Contest.},
address = {Philadelphia, Pennsylvania, USA},
keywords = {Acc. Int. E-A, MIPRCV, PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
2007
Rizo, D.; Iñesta, J. M.; León, P. J. Ponce De
Towards a human-friendly melody characterization by automatically induced rules Proceedings Article
In: Dixon, Rainer Typke David Bainbridge Simon (Ed.): Proceedings of the 8th Int. Conf. on Music Information Retrieval, ISMIR 2007, pp. 437–440, Austrian Computer Society Austrian Computer Society, Vienna, 2007, ISBN: 978-3-85403-218-2.
Abstract | Links | BibTeX | Tags: GV-JRRJ, PROSEMUS
@inproceedings{k197,
title = {Towards a human-friendly melody characterization by automatically induced rules},
author = {D. Rizo and J. M. Iñesta and P. J. Ponce De León},
editor = {Rainer Typke David Bainbridge Simon Dixon},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/197/ismir2007.pdf},
isbn = {978-3-85403-218-2},
year = {2007},
date = {2007-09-01},
urldate = {2007-09-01},
booktitle = {Proceedings of the 8th Int. Conf. on Music Information Retrieval, ISMIR 2007},
pages = {437--440},
publisher = {Austrian Computer Society},
address = {Vienna},
organization = {Austrian Computer Society},
abstract = {There is an increasing interest in music information retrieval for reference, motive, or thumbnail extraction from a piece in order to have a compact and representative representation of the information to be retrieved. One of the main references for music is its melody. In a practical environment of symbolic format collections the information can be found in standard MIDI file format, structured as a number of tracks, usually one of them containing the melodic line, while the others contain the accompaniment. The goal of this work is to analyse how statistical rules can be used to characterize a melody in such a way that one can understand the solution of an automatic system for selecting the track containing the melody in such files.},
keywords = {GV-JRRJ, PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
There is an increasing interest in music information retrieval for reference, motive, or thumbnail extraction from a piece in order to have a compact and representative representation of the information to be retrieved. One of the main references for music is its melody. In a practical environment of symbolic format collections the information can be found in standard MIDI file format, structured as a number of tracks, usually one of them containing the melodic line, while the others contain the accompaniment. The goal of this work is to analyse how statistical rules can be used to characterize a melody in such a way that one can understand the solution of an automatic system for selecting the track containing the melody in such files. Pertusa, A.; Maestre, E.; Ramírez, R.
Identifying saxophonists from their playing styles Proceedings Article
In: Proc. of the 30th Audio Engineering Society (AES) Conference, Saariselkä, Finland, 2007.
@inproceedings{k187,
title = {Identifying saxophonists from their playing styles},
author = {A. Pertusa and E. Maestre and R. Ramírez},
year = {2007},
date = {2007-01-01},
urldate = {2007-01-01},
booktitle = {Proc. of the 30th Audio Engineering Society (AES) Conference},
address = {Saariselkä, Finland},
keywords = {PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
Pertusa, A.; Maestre, E.; Ramírez, R.
Performance-based Interpreter Identification in Saxophone Audio Recordings Journal Article
In: IEEE Transactions on Circuits and Systems for Video Technology, vol. 7, no. 3, pp. 356-364, 2007.
@article{k194,
title = {Performance-based Interpreter Identification in Saxophone Audio Recordings},
author = {A. Pertusa and E. Maestre and R. Ramírez},
year = {2007},
date = {2007-01-01},
urldate = {2007-01-01},
journal = {IEEE Transactions on Circuits and Systems for Video Technology},
volume = {7},
number = {3},
pages = {356-364},
keywords = {PROSEMUS},
pubstate = {published},
tppubtype = {article}
}
Pertusa, A.; Rauber, A.; Lidy, T.; Iñesta, J. M.
Improving genre classification by combination of audio and symbolic descriptors using a transcription system Proceedings Article
In: Proc. of the 8th Int. Conf. on Music Information Retrieval, ISMIR 2007, pp. 61-66, Vienna, Austria, 2007.
Links | BibTeX | Tags: Acc. Int. E-A, PROSEMUS
@inproceedings{k196,
title = {Improving genre classification by combination of audio and symbolic descriptors using a transcription system},
author = {A. Pertusa and A. Rauber and T. Lidy and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/196/aptl_ismir07.pdf},
year = {2007},
date = {2007-01-01},
urldate = {2007-01-01},
booktitle = {Proc. of the 8th Int. Conf. on Music Information Retrieval, ISMIR 2007},
pages = {61-66},
address = {Vienna, Austria},
keywords = {Acc. Int. E-A, PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
Iñesta, J. M.; Rizo, D.; Illescas, P. R.
Harmonic, melodic, and functional automatic analysis Proceedings Article
In: Proceedings of the 2007 International Computer Music Conferrence, pp. 165–168, 2007, ISBN: 2223-3881.
Abstract | Links | BibTeX | Tags: GV-JRRJ, PROSEMUS
@inproceedings{k201,
title = {Harmonic, melodic, and functional automatic analysis},
author = {J. M. Iñesta and D. Rizo and P. R. Illescas},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/201/icmc07_tonal_analysis.pdf},
isbn = {2223-3881},
year = {2007},
date = {2007-01-01},
urldate = {2007-01-01},
booktitle = {Proceedings of the 2007 International Computer Music Conferrence},
pages = {165--168},
abstract = {This work is an effort towards the development of a system for the automation of
traditional harmonic analysis of polyphonic scores in symbolic format. A number of stages have been
designed in this procedure: melodic analysis of harmonic and non-harmonic tones, vertical harmonic analysis, tonality, and tonal functions. All these informations are
represented as a weighted directed acyclic graph. The best possible analysis is
the path that maximizes the sum of weights in the graph, obtained through a
dynamic programming algorithm. The feasibility of this approach has been tested on six J.S. Bach's harmonized chorales},
keywords = {GV-JRRJ, PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
This work is an effort towards the development of a system for the automation of
traditional harmonic analysis of polyphonic scores in symbolic format. A number of stages have been
designed in this procedure: melodic analysis of harmonic and non-harmonic tones, vertical harmonic analysis, tonality, and tonal functions. All these informations are
represented as a weighted directed acyclic graph. The best possible analysis is
the path that maximizes the sum of weights in the graph, obtained through a
dynamic programming algorithm. The feasibility of this approach has been tested on six J.S. Bach's harmonized chorales
2010
Pérez-Sancho, C.; Rizo, D.; Iñesta, J. M.; León, P. J. Ponce; Kersten, S.; Ramírez, R.
Genre classification of music by tonal harmony Journal Article
In: Intelligent Data Analysis, vol. 14, no. 5, pp. 533-545, 2010, ISSN: 1088-467X.
Abstract | BibTeX | Tags: Acc. Int. E-A, DRIMS, PROSEMUS
@article{k232,
title = {Genre classification of music by tonal harmony},
author = {C. Pérez-Sancho and D. Rizo and J. M. Iñesta and P. J. Ponce León and S. Kersten and R. Ramírez},
issn = {1088-467X},
year = {2010},
date = {2010-09-01},
urldate = {2010-09-01},
journal = {Intelligent Data Analysis},
volume = {14},
number = {5},
pages = {533-545},
abstract = {In this paper we present a genre classification framework for audio music based on a symbolic classification system. Audio signals are transformed into a symbolic representation of harmony using a chord transcription algorithm, based on the computation of harmonic pitch class profiles. Then, language models built from a ground truth of chord progressions for each genre are used to perform classification. We show that chord progressions are a suitable feature to represent musical genre, as they capture the harmonic rules relevant in each musical period or style. Finally, results using both pure symbolic information and chords transcribed from audio-from-MIDI are compared, in order to evaluate the effects of the transcription process in this task.},
keywords = {Acc. Int. E-A, DRIMS, PROSEMUS},
pubstate = {published},
tppubtype = {article}
}
In this paper we present a genre classification framework for audio music based on a symbolic classification system. Audio signals are transformed into a symbolic representation of harmony using a chord transcription algorithm, based on the computation of harmonic pitch class profiles. Then, language models built from a ground truth of chord progressions for each genre are used to perform classification. We show that chord progressions are a suitable feature to represent musical genre, as they capture the harmonic rules relevant in each musical period or style. Finally, results using both pure symbolic information and chords transcribed from audio-from-MIDI are compared, in order to evaluate the effects of the transcription process in this task.
2009
Rizo, D.; Lemström, K.; Iñesta, J. M.
Ensemble of state-of-the-art methods for polyphonic music comparison Proceedings Article
In: Rauber, A.; Orio, N.; Rizo, D. (Ed.): Proceedings of the Workshop on Exploring Musical Information Spaces, ECDL 2009, pp. 46–51, Corfu, Greece, 2009, ISBN: 978-84-692-6082-1.
Abstract | Links | BibTeX | Tags: PROSEMUS
@inproceedings{k233,
title = {Ensemble of state-of-the-art methods for polyphonic music comparison},
author = {D. Rizo and K. Lemström and J. M. Iñesta},
editor = {A. Rauber and N. Orio and D. Rizo},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/233/wemis2009.pdf},
isbn = {978-84-692-6082-1},
year = {2009},
date = {2009-10-01},
urldate = {2009-10-01},
booktitle = {Proceedings of the Workshop on Exploring Musical Information Spaces, ECDL 2009},
pages = {46--51},
address = {Corfu, Greece},
abstract = {Content-based music comparison is a task where no musical similarity measure can perform well in all possible cases. In this paper we will show that a careful combination of different similarity measures in an ensemble measure, will behave more robust than any of the included individual measures when applied as stand-alone measures. For the experiments we have used five state-of-the-art polyphonic similarity measures and three different corpora of polyphonic music.},
keywords = {PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
Content-based music comparison is a task where no musical similarity measure can perform well in all possible cases. In this paper we will show that a careful combination of different similarity measures in an ensemble measure, will behave more robust than any of the included individual measures when applied as stand-alone measures. For the experiments we have used five state-of-the-art polyphonic similarity measures and three different corpora of polyphonic music.
Iñesta, J. M.; Pérez-García, T.; Rizo, D.
metamidi: a tool for automatic metadata extraction from MIDI files Proceedings Article
In: Rauber, A.; Orio, N.; Rizo, D. (Ed.): Proceedings of the Workshop on Exploring Musical Information Spaces, ECDL 2009, pp. 36–40, Corfu, Greece, 2009, ISBN: 978-84-692-6082-1.
Abstract | Links | BibTeX | Tags: PROSEMUS
@inproceedings{k234,
title = {metamidi: a tool for automatic metadata extraction from MIDI files},
author = {J. M. Iñesta and T. Pérez-García and D. Rizo},
editor = {A. Rauber and N. Orio and D. Rizo},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/234/metamidi.pdf},
isbn = {978-84-692-6082-1},
year = {2009},
date = {2009-10-01},
urldate = {2009-10-01},
booktitle = {Proceedings of the Workshop on Exploring Musical Information Spaces, ECDL 2009},
pages = {36--40},
address = {Corfu, Greece},
abstract = {The increasing availability of on-line music has motivated a growing interest for organizing, commercializing, and delivering this kind of multimedia content. For it, the use of metadata is of utmost importance. Metadata permit organization, indexing, and retrieval of music contents. They are, therefore, a subject of research both from the design and automatic extraction approaches. The present work focuses on this second issue, providing an open source tool for metadata extraction from standard MIDI files. The tool is presented, the utilized metadata are explained, and some applications and experiments are described as examples of its capabilities.},
keywords = {PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
The increasing availability of on-line music has motivated a growing interest for organizing, commercializing, and delivering this kind of multimedia content. For it, the use of metadata is of utmost importance. Metadata permit organization, indexing, and retrieval of music contents. They are, therefore, a subject of research both from the design and automatic extraction approaches. The present work focuses on this second issue, providing an open source tool for metadata extraction from standard MIDI files. The tool is presented, the utilized metadata are explained, and some applications and experiments are described as examples of its capabilities.
Pérez-Sancho, C.
Stochastic Language Models for Music Information Retrieval PhD Thesis
2009.
Abstract | Links | BibTeX | Tags: Acc. Int. E-A, PROSEMUS
@phdthesis{k240,
title = {Stochastic Language Models for Music Information Retrieval},
author = {C. Pérez-Sancho},
editor = {Jorge Calera Rubio José M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/240/phdthesis_cperez.pdf},
year = {2009},
date = {2009-07-01},
address = {Alicante, Spain},
organization = {Universidad de Alicante},
abstract = {Music Information Retrieval (MIR) is an interdisciplinary research area that aims at providing solutions to most problems related to the access to multimedia databases, in particular those with musical content, either in symbolic (MIDI) or audio format. An especially relevant problem is the automatic organization and indexation of data, since carrying out these tasks by hand would require an overwhelming effort for most people and institutions.
One of the most relevant features that can be obtained from a song in order to perform its automatic organization is the musical style, since it is one of the most popular fields used by people when accessing musical databases and catalogs. In this thesis it has been studied to what extent the musical style of a piece can be determined using just the information contained in its score, by applying pattern recognition techniques on melodic and harmonic sequences obtained from musical scores.},
keywords = {Acc. Int. E-A, PROSEMUS},
pubstate = {published},
tppubtype = {phdthesis}
}
Music Information Retrieval (MIR) is an interdisciplinary research area that aims at providing solutions to most problems related to the access to multimedia databases, in particular those with musical content, either in symbolic (MIDI) or audio format. An especially relevant problem is the automatic organization and indexation of data, since carrying out these tasks by hand would require an overwhelming effort for most people and institutions.
One of the most relevant features that can be obtained from a song in order to perform its automatic organization is the musical style, since it is one of the most popular fields used by people when accessing musical databases and catalogs. In this thesis it has been studied to what extent the musical style of a piece can be determined using just the information contained in its score, by applying pattern recognition techniques on melodic and harmonic sequences obtained from musical scores.
One of the most relevant features that can be obtained from a song in order to perform its automatic organization is the musical style, since it is one of the most popular fields used by people when accessing musical databases and catalogs. In this thesis it has been studied to what extent the musical style of a piece can be determined using just the information contained in its score, by applying pattern recognition techniques on melodic and harmonic sequences obtained from musical scores.
Pérez-Sancho, C.; Rizo, D.; Iñesta, J. M.
Genre classification using chords and stochastic language models Journal Article
In: Connection Science, vol. 21, no. 2, pp. 145-159, 2009, ISSN: 0954-0091.
Abstract | BibTeX | Tags: Acc. Int. E-A, MIPRCV, PROSEMUS
@article{k227,
title = {Genre classification using chords and stochastic language models},
author = {C. Pérez-Sancho and D. Rizo and J. M. Iñesta},
issn = {0954-0091},
year = {2009},
date = {2009-05-01},
urldate = {2009-05-01},
journal = {Connection Science},
volume = {21},
number = {2},
pages = {145-159},
abstract = {Music genre meta-data is of paramount importance for the organisation of music repositories. People use genre in a natural way when entering a music store or looking into music collections. Automatic genre classification has become a popular topic in music information retrieval research both, with digital audio and symbolic data. This work focuses on the symbolic approach, bringing to music cognition some technologies, like the stochastic language models, already successfully applied to text categorisation. The representation chosen here is to model chord progressions as n-grams and strings and then apply perplexity and naiumlve Bayes classifiers, respectively, in order to assess how often those structures are found in the target genres. Some genres and sub-genres among popular, jazz, and academic music have been considered, trying to investigate how far can we reach using harmonic information with these models. The results at different levels of the genre hierarchy for the techniques employed are presented and discussed.},
keywords = {Acc. Int. E-A, MIPRCV, PROSEMUS},
pubstate = {published},
tppubtype = {article}
}
Music genre meta-data is of paramount importance for the organisation of music repositories. People use genre in a natural way when entering a music store or looking into music collections. Automatic genre classification has become a popular topic in music information retrieval research both, with digital audio and symbolic data. This work focuses on the symbolic approach, bringing to music cognition some technologies, like the stochastic language models, already successfully applied to text categorisation. The representation chosen here is to model chord progressions as n-grams and strings and then apply perplexity and naiumlve Bayes classifiers, respectively, in order to assess how often those structures are found in the target genres. Some genres and sub-genres among popular, jazz, and academic music have been considered, trying to investigate how far can we reach using harmonic information with these models. The results at different levels of the genre hierarchy for the techniques employed are presented and discussed.
Rizo, D.; Lemström, K.; Iñesta, J. M.
Tree representation in combined polyphonic music comparison Journal Article
In: Computer Music Modeling and Retrieval. Genesis of Meaning in Sound and Music. Lecture Notes in Computer Science, vol. 5493, pp. 177–195, 2009, ISSN: 978-3-642-02517-4.
Abstract | BibTeX | Tags: PROSEMUS
@article{k229,
title = {Tree representation in combined polyphonic music comparison},
author = {D. Rizo and K. Lemström and J. M. Iñesta},
issn = {978-3-642-02517-4},
year = {2009},
date = {2009-01-01},
journal = {Computer Music Modeling and Retrieval. Genesis of Meaning in Sound and Music. Lecture Notes in Computer Science},
volume = {5493},
pages = {177--195},
abstract = {Identifying copies or different versions of a same musical work is a focal problem in maintaining large music databases. In this paper we introduce novel ideas and methods that are applicable to metered, symbolically encoded polyphonic music. We show how to represent and compare polyphonic music using a tree structure. Moreover, we put for trial various comparison methods and observe whether better comparison results can be obtained by combining distinct similarity measures. Our experiments show that the proposed representation is adequate for the task with good quality results and processing times, and when combined with other methods it becomes more robust against various types of music.},
keywords = {PROSEMUS},
pubstate = {published},
tppubtype = {article}
}
Identifying copies or different versions of a same musical work is a focal problem in maintaining large music databases. In this paper we introduce novel ideas and methods that are applicable to metered, symbolically encoded polyphonic music. We show how to represent and compare polyphonic music using a tree structure. Moreover, we put for trial various comparison methods and observe whether better comparison results can be obtained by combining distinct similarity measures. Our experiments show that the proposed representation is adequate for the task with good quality results and processing times, and when combined with other methods it becomes more robust against various types of music.
Calera-Rubio, J.; Bernabeu, J. F.
A probabilistic approach to melodic similarity Proceedings Article
In: Proceedings of MML 2009, pp. 48-53, 2009.
Abstract | Links | BibTeX | Tags: ARFAI, DRIMS, MIPRCV, PROSEMUS, TIASA
@inproceedings{k231,
title = {A probabilistic approach to melodic similarity},
author = {J. Calera-Rubio and J. F. Bernabeu},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/231/mml2009Bernabeu.pdf},
year = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
booktitle = {Proceedings of MML 2009},
pages = {48-53},
abstract = {Melodic similarity is an important research topic in music information retrieval.
The representation of symbolic music by means of trees has proven to be suitable
in melodic similarity computation, because they are able to code rhythm in their
structure leaving only pitch representations as a degree of freedom for coding.
In order to compare trees, different edit distances have been previously used.
In this paper, stochastic k-testable tree-models, formerly used in other domains
like structured document compression or natural language processing, have been
used for computing a similarity measure between melody trees as a probability
and their performance has been compared to a classical tree edit distance.},
keywords = {ARFAI, DRIMS, MIPRCV, PROSEMUS, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
Melodic similarity is an important research topic in music information retrieval.
The representation of symbolic music by means of trees has proven to be suitable
in melodic similarity computation, because they are able to code rhythm in their
structure leaving only pitch representations as a degree of freedom for coding.
In order to compare trees, different edit distances have been previously used.
In this paper, stochastic k-testable tree-models, formerly used in other domains
like structured document compression or natural language processing, have been
used for computing a similarity measure between melody trees as a probability
and their performance has been compared to a classical tree edit distance.
The representation of symbolic music by means of trees has proven to be suitable
in melodic similarity computation, because they are able to code rhythm in their
structure leaving only pitch representations as a degree of freedom for coding.
In order to compare trees, different edit distances have been previously used.
In this paper, stochastic k-testable tree-models, formerly used in other domains
like structured document compression or natural language processing, have been
used for computing a similarity measure between melody trees as a probability
and their performance has been compared to a classical tree edit distance.
Pertusa, A.; Iñesta, J. M.
Note Onset Detection Using One Semitone Filter-Bank For MIREX 2009 Proceedings Article
In: MIREX 2009 - Music Information Retrieval Evaluation eXchange, MIREX Audio Onset Detection, Kobe, Japan., 2009.
Links | BibTeX | Tags: Acc. Int. E-A, PROSEMUS
@inproceedings{k238,
title = {Note Onset Detection Using One Semitone Filter-Bank For MIREX 2009},
author = {A. Pertusa and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/238/PI.pdf},
year = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
booktitle = {MIREX 2009 - Music Information Retrieval Evaluation eXchange, MIREX Audio Onset Detection},
address = {Kobe, Japan.},
keywords = {Acc. Int. E-A, PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
2008
León, P. J. Ponce; Iñesta, J. M.; Rizo, D.
Mining digital music score collections: melody extraction and genre recognition Book Chapter
In: Yin, Peng-Yeng (Ed.): Pattern Recognition, Chapter 25, pp. 559-590, IN-TECH, Vienna, Austria, 2008, ISBN: 978-3-902613-24-4.
Abstract | BibTeX | Tags: PROSEMUS
@inbook{k225,
title = {Mining digital music score collections: melody extraction and genre recognition},
author = {P. J. Ponce León and J. M. Iñesta and D. Rizo},
editor = {Peng-Yeng Yin},
isbn = {978-3-902613-24-4},
year = {2008},
date = {2008-11-01},
urldate = {2008-11-01},
booktitle = {Pattern Recognition},
pages = {559-590},
publisher = {IN-TECH},
address = {Vienna, Austria},
chapter = {25},
abstract = {In the field of computer music, pattern recognition algorithms are very
relevant for music information retrieval (MIR) applications. Two challenging
tasks in this area is the automatic recognition of musical genre and melody extraction, having a
number of applications like indexing and selecting musical databases.
One of the main references for music is its melody. In a practical environment of digital music score collections the information can be found in standard MIDI file format. Music is structured as a number of tracks in this file format, usually one of them containing the melodic line, while others tracks contain the accompaniment.
Finding that melody track is very useful for a number of applications, like speeding up melody
matching when searching in MIDI databases, extracting motifs for musicological analysis, building
music thumbnails or extracting melodic ringtones from MIDI files.
In the first part of this chapter,
musical content information is modeled by computing global statistical descriptors from track content.
These descriptors are the input to a random forest classifier
that assigns the probability of being a melodic line to each track. The
track with the highest probability is then selected as the one containing the
melodic line of the MIDI file. The first part of this chapter ends with a discussion on results obtained from a number of databases of different music styles.
The second part of the chapter deals with the problem of classifying such melodies in a collection of music genres. A slightly different approach is used for this task, first dividing a melody track in segments of fixed length. Statistical features are extracted for each segment and used to classify them as one of several genres.
The proposed methodology
is presented, covering the feature extraction, feature selection,
and genre classification stages. Different supervised classification
methods, like Bayesian classifier and nearest neighbors are applied. As a proof of concept, the performance of such algorithms against different description models and parameters is analyzed for two particular musical genres, like jazz and classical music.},
keywords = {PROSEMUS},
pubstate = {published},
tppubtype = {inbook}
}
In the field of computer music, pattern recognition algorithms are very
relevant for music information retrieval (MIR) applications. Two challenging
tasks in this area is the automatic recognition of musical genre and melody extraction, having a
number of applications like indexing and selecting musical databases.
One of the main references for music is its melody. In a practical environment of digital music score collections the information can be found in standard MIDI file format. Music is structured as a number of tracks in this file format, usually one of them containing the melodic line, while others tracks contain the accompaniment.
Finding that melody track is very useful for a number of applications, like speeding up melody
matching when searching in MIDI databases, extracting motifs for musicological analysis, building
music thumbnails or extracting melodic ringtones from MIDI files.
In the first part of this chapter,
musical content information is modeled by computing global statistical descriptors from track content.
These descriptors are the input to a random forest classifier
that assigns the probability of being a melodic line to each track. The
track with the highest probability is then selected as the one containing the
melodic line of the MIDI file. The first part of this chapter ends with a discussion on results obtained from a number of databases of different music styles.
The second part of the chapter deals with the problem of classifying such melodies in a collection of music genres. A slightly different approach is used for this task, first dividing a melody track in segments of fixed length. Statistical features are extracted for each segment and used to classify them as one of several genres.
The proposed methodology
is presented, covering the feature extraction, feature selection,
and genre classification stages. Different supervised classification
methods, like Bayesian classifier and nearest neighbors are applied. As a proof of concept, the performance of such algorithms against different description models and parameters is analyzed for two particular musical genres, like jazz and classical music.
relevant for music information retrieval (MIR) applications. Two challenging
tasks in this area is the automatic recognition of musical genre and melody extraction, having a
number of applications like indexing and selecting musical databases.
One of the main references for music is its melody. In a practical environment of digital music score collections the information can be found in standard MIDI file format. Music is structured as a number of tracks in this file format, usually one of them containing the melodic line, while others tracks contain the accompaniment.
Finding that melody track is very useful for a number of applications, like speeding up melody
matching when searching in MIDI databases, extracting motifs for musicological analysis, building
music thumbnails or extracting melodic ringtones from MIDI files.
In the first part of this chapter,
musical content information is modeled by computing global statistical descriptors from track content.
These descriptors are the input to a random forest classifier
that assigns the probability of being a melodic line to each track. The
track with the highest probability is then selected as the one containing the
melodic line of the MIDI file. The first part of this chapter ends with a discussion on results obtained from a number of databases of different music styles.
The second part of the chapter deals with the problem of classifying such melodies in a collection of music genres. A slightly different approach is used for this task, first dividing a melody track in segments of fixed length. Statistical features are extracted for each segment and used to classify them as one of several genres.
The proposed methodology
is presented, covering the feature extraction, feature selection,
and genre classification stages. Different supervised classification
methods, like Bayesian classifier and nearest neighbors are applied. As a proof of concept, the performance of such algorithms against different description models and parameters is analyzed for two particular musical genres, like jazz and classical music.
León, P. J. Ponce; Rizo, D.; Ramírez, R.; Iñesta, J. M.
Melody Characterization by a Genetic Fuzzy System Proceedings Article
In: Supper, Martin; Weinzierl, Stefan (Ed.): Proceedings of the 5th Sound and Music Computing Conference, pp. 15-23, Universitätsverlag der TU Berlin, 2008, ISBN: 978-3-7983-2094-9.
Abstract | Links | BibTeX | Tags: PROSEMUS
@inproceedings{k212,
title = {Melody Characterization by a Genetic Fuzzy System},
author = {P. J. Ponce León and D. Rizo and R. Ramírez and J. M. Iñesta},
editor = {Martin Supper and Stefan Weinzierl},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/212/smc-08.pdf},
isbn = {978-3-7983-2094-9},
year = {2008},
date = {2008-07-01},
urldate = {2008-07-01},
booktitle = {Proceedings of the 5th Sound and Music Computing Conference},
pages = {15-23},
publisher = {Universitätsverlag der TU Berlin},
abstract = {We present preliminary work on automatic human-readable melody
characterization. In order to obtain such a characterization, we
(1) extract a set of statistical descriptors from the tracks in a dataset of MIDI files,
(2) apply a rule induction algorithm to obtain a set of (crisp) classification
rules for melody track identification, and (3) automatically transform the
crisp rules into fuzzy rules by applying a genetic algorithm to generate the membership
functions for the rule attributes.
Some results are presented and discussed.},
keywords = {PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
We present preliminary work on automatic human-readable melody
characterization. In order to obtain such a characterization, we
(1) extract a set of statistical descriptors from the tracks in a dataset of MIDI files,
(2) apply a rule induction algorithm to obtain a set of (crisp) classification
rules for melody track identification, and (3) automatically transform the
crisp rules into fuzzy rules by applying a genetic algorithm to generate the membership
functions for the rule attributes.
Some results are presented and discussed.
characterization. In order to obtain such a characterization, we
(1) extract a set of statistical descriptors from the tracks in a dataset of MIDI files,
(2) apply a rule induction algorithm to obtain a set of (crisp) classification
rules for melody track identification, and (3) automatically transform the
crisp rules into fuzzy rules by applying a genetic algorithm to generate the membership
functions for the rule attributes.
Some results are presented and discussed.
Lemström, K.; Rizo, D.
Tree structured and combined methods for comparing metered polyphonic music Proceedings Article
In: Proc. Computer Music Modeling and Retrieval 2008 (CMMR'08), pp. 263–278, Copenhagen, Denmark, 2008, ISBN: 978-87-7606-027-5.
@inproceedings{k206,
title = {Tree structured and combined methods for comparing metered polyphonic music},
author = {K. Lemström and D. Rizo},
isbn = {978-87-7606-027-5},
year = {2008},
date = {2008-05-01},
urldate = {2008-05-01},
booktitle = {Proc. Computer Music Modeling and Retrieval 2008 (CMMR'08)},
pages = {263--278},
address = {Copenhagen, Denmark},
keywords = {PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
Iñesta, J. M.; Pertusa, A.
Multiple Fundamental Frequency estimation using Gaussian smoothness Proceedings Article
In: Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP 2008, pp. 105-108, Las Vegas, USA, 2008, ISBN: 1-4244-1484-9.
Links | BibTeX | Tags: Acc. Int. E-A, MIPRCV, PROSEMUS
@inproceedings{k202,
title = {Multiple Fundamental Frequency estimation using Gaussian smoothness},
author = {J. M. Iñesta and A. Pertusa},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/202/2974_sent.pdf},
isbn = {1-4244-1484-9},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
booktitle = {Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP 2008},
pages = {105-108},
address = {Las Vegas, USA},
keywords = {Acc. Int. E-A, MIPRCV, PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
Ramírez, R.; Pérez, A.; Maestre, E.; Kersten, S.; Rizo, D.; Illescas, P. R.; Iñesta, J. M.
Modeling celtic violin expressive performance Proceedings Article
In: Proc. Int. Workshop on Machine Learning and Music, MML 2008, pp. 7-8, Helsinki, Finland, 2008.
@inproceedings{k208,
title = {Modeling celtic violin expressive performance},
author = {R. Ramírez and A. Pérez and E. Maestre and S. Kersten and D. Rizo and P. R. Illescas and J. M. Iñesta},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
booktitle = {Proc. Int. Workshop on Machine Learning and Music, MML 2008},
pages = {7-8},
address = {Helsinki, Finland},
keywords = {PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
Pérez-Sancho, C.; Rizo, D.; Kersten, S.; Ramírez, R.
Genre classification of music by tonal harmony Proceedings Article
In: Proc. Int. Workshop on Machine Learning and Music, MML 2008, pp. 21-22, Helsinki, Finland, 2008.
BibTeX | Tags: Acc. Int. E-A, MIPRCV, PROSEMUS
@inproceedings{k209,
title = {Genre classification of music by tonal harmony},
author = {C. Pérez-Sancho and D. Rizo and S. Kersten and R. Ramírez},
year = {2008},
date = {2008-01-01},
booktitle = {Proc. Int. Workshop on Machine Learning and Music, MML 2008},
pages = {21-22},
address = {Helsinki, Finland},
keywords = {Acc. Int. E-A, MIPRCV, PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
Rizo, D.; Illescas, P. R.
Learning to analyse tonal music Proceedings Article
In: Proc. Int. Workshop on Machine Learning and Music, MML 2008, pp. 25-26, Helsinki, Finland, 2008.
BibTeX | Tags: MIPRCV, PROSEMUS
@inproceedings{k210,
title = {Learning to analyse tonal music},
author = {D. Rizo and P. R. Illescas},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
booktitle = {Proc. Int. Workshop on Machine Learning and Music, MML 2008},
pages = {25-26},
address = {Helsinki, Finland},
keywords = {MIPRCV, PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
León, P. J. Ponce; Rizo, D.; Iñesta, J. M.
Melody characterization by a fuzzy rule system Proceedings Article
In: Proc. Int. Workshop on Machine Learning and Music, MML 2008, pp. 35-36, Helsinki, Finland, 2008.
@inproceedings{k211,
title = {Melody characterization by a fuzzy rule system},
author = {P. J. Ponce León and D. Rizo and J. M. Iñesta},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
booktitle = {Proc. Int. Workshop on Machine Learning and Music, MML 2008},
pages = {35-36},
address = {Helsinki, Finland},
keywords = {PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
Iñesta, J. M.; Heredia-Agoiz, J. L.; León, P. J. Ponce
A ground-truth experiment on melody genre recognition in absence of timbre Proceedings Article
In: Proc. of the 10th International Conference on Music Perception and Cognition (ICMPC10), pp. 758-761, Sapporo, Japan, 2008, ISBN: 978-4-9904208-0-2.
BibTeX | Tags: Acc. Int. E-A, PROSEMUS
@inproceedings{k215,
title = {A ground-truth experiment on melody genre recognition in absence of timbre},
author = {J. M. Iñesta and J. L. Heredia-Agoiz and P. J. Ponce León},
isbn = {978-4-9904208-0-2},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
booktitle = {Proc. of the 10th International Conference on Music Perception and Cognition (ICMPC10)},
pages = {758-761},
address = {Sapporo, Japan},
keywords = {Acc. Int. E-A, PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
Pérez-Sancho, C.; Rizo, D.; Iñesta, J. M.
Stochastic text models for music categorization Journal Article
In: Lecture Notes in Computer Science, vol. 5342, pp. 55-64, 2008.
Links | BibTeX | Tags: Acc. Int. E-A, MIPRCV, PROSEMUS
@article{k217,
title = {Stochastic text models for music categorization},
author = {C. Pérez-Sancho and D. Rizo and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/217/music-cat.pdf},
year = {2008},
date = {2008-01-01},
journal = {Lecture Notes in Computer Science},
volume = {5342},
pages = {55-64},
keywords = {Acc. Int. E-A, MIPRCV, PROSEMUS},
pubstate = {published},
tppubtype = {article}
}
Habrard, A.; Iñesta, J. M.; Rizo, D.; Sebban, M.
Melody recognition with learned edit distances Journal Article
In: Lecture Notes in Computer Science, vol. 5342, pp. 86-96, 2008.
Links | BibTeX | Tags: MIPRCV, PROSEMUS
@article{k218,
title = {Melody recognition with learned edit distances},
author = {A. Habrard and J. M. Iñesta and D. Rizo and M. Sebban},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/218/melody-rec.pdf},
year = {2008},
date = {2008-01-01},
journal = {Lecture Notes in Computer Science},
volume = {5342},
pages = {86-96},
keywords = {MIPRCV, PROSEMUS},
pubstate = {published},
tppubtype = {article}
}
Lidy, T.; Rauber, A.; Pertusa, A.; de León, P. J. Ponce; Iñesta, J. M.
MIREX 2008: Audio Music Classification Using A Combination Of Spectral, Timbral, Rhythmic, Temporal And Symbolic Features. Proceedings Article
In: MIREX 2008 - Music Information Retrieval Evaluation eXchange, MIREX Genre Classification Contest., Philadelphia, Pennsylvania, USA, 2008.
Abstract | Links | BibTeX | Tags: Acc. Int. E-A, PROSEMUS
@inproceedings{k236,
title = {MIREX 2008: Audio Music Classification Using A Combination Of Spectral, Timbral, Rhythmic, Temporal And Symbolic Features.},
author = {T. Lidy and A. Rauber and A. Pertusa and P. J. Ponce de León and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/236/abstract_mirex08_class.pdf},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
booktitle = {MIREX 2008 - Music Information Retrieval Evaluation eXchange, MIREX Genre Classification Contest.},
address = {Philadelphia, Pennsylvania, USA},
abstract = {The novel approach of combining audio and symbolic features
for music classification from audio enhanced previous
audio-only based results in MIREX 2007. We extended the
approach by including temporal audio features, enhancing
the polyphonic audio to MIDI transcription system and including
an extended set of symbolic features. Recent research
in music genre classification hints at a glass ceiling
being reached using timbral audio features.},
keywords = {Acc. Int. E-A, PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
The novel approach of combining audio and symbolic features
for music classification from audio enhanced previous
audio-only based results in MIREX 2007. We extended the
approach by including temporal audio features, enhancing
the polyphonic audio to MIDI transcription system and including
an extended set of symbolic features. Recent research
in music genre classification hints at a glass ceiling
being reached using timbral audio features.
for music classification from audio enhanced previous
audio-only based results in MIREX 2007. We extended the
approach by including temporal audio features, enhancing
the polyphonic audio to MIDI transcription system and including
an extended set of symbolic features. Recent research
in music genre classification hints at a glass ceiling
being reached using timbral audio features.
Pertusa, A.; Iñesta, J. M.
Multiple Fundamental Frequency Estimation Using Gaussian Smoothness And Short Context. Proceedings Article
In: MIREX 2008 - Music Information Retrieval Evaluation eXchange, MIREX Fundamental Frequency Estimation & Tracking Contest., Philadelphia, Pennsylvania, USA, 2008.
Links | BibTeX | Tags: Acc. Int. E-A, MIPRCV, PROSEMUS
@inproceedings{k237,
title = {Multiple Fundamental Frequency Estimation Using Gaussian Smoothness And Short Context.},
author = {A. Pertusa and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/237/F0_pertusa.pdf},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
booktitle = {MIREX 2008 - Music Information Retrieval Evaluation eXchange, MIREX Fundamental Frequency Estimation & Tracking Contest.},
address = {Philadelphia, Pennsylvania, USA},
keywords = {Acc. Int. E-A, MIPRCV, PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
2007
Rizo, D.; Iñesta, J. M.; León, P. J. Ponce De
Towards a human-friendly melody characterization by automatically induced rules Proceedings Article
In: Dixon, Rainer Typke David Bainbridge Simon (Ed.): Proceedings of the 8th Int. Conf. on Music Information Retrieval, ISMIR 2007, pp. 437–440, Austrian Computer Society Austrian Computer Society, Vienna, 2007, ISBN: 978-3-85403-218-2.
Abstract | Links | BibTeX | Tags: GV-JRRJ, PROSEMUS
@inproceedings{k197,
title = {Towards a human-friendly melody characterization by automatically induced rules},
author = {D. Rizo and J. M. Iñesta and P. J. Ponce De León},
editor = {Rainer Typke David Bainbridge Simon Dixon},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/197/ismir2007.pdf},
isbn = {978-3-85403-218-2},
year = {2007},
date = {2007-09-01},
urldate = {2007-09-01},
booktitle = {Proceedings of the 8th Int. Conf. on Music Information Retrieval, ISMIR 2007},
pages = {437--440},
publisher = {Austrian Computer Society},
address = {Vienna},
organization = {Austrian Computer Society},
abstract = {There is an increasing interest in music information retrieval for reference, motive, or thumbnail extraction from a piece in order to have a compact and representative representation of the information to be retrieved. One of the main references for music is its melody. In a practical environment of symbolic format collections the information can be found in standard MIDI file format, structured as a number of tracks, usually one of them containing the melodic line, while the others contain the accompaniment. The goal of this work is to analyse how statistical rules can be used to characterize a melody in such a way that one can understand the solution of an automatic system for selecting the track containing the melody in such files.},
keywords = {GV-JRRJ, PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
There is an increasing interest in music information retrieval for reference, motive, or thumbnail extraction from a piece in order to have a compact and representative representation of the information to be retrieved. One of the main references for music is its melody. In a practical environment of symbolic format collections the information can be found in standard MIDI file format, structured as a number of tracks, usually one of them containing the melodic line, while the others contain the accompaniment. The goal of this work is to analyse how statistical rules can be used to characterize a melody in such a way that one can understand the solution of an automatic system for selecting the track containing the melody in such files.
Pertusa, A.; Maestre, E.; Ramírez, R.
Identifying saxophonists from their playing styles Proceedings Article
In: Proc. of the 30th Audio Engineering Society (AES) Conference, Saariselkä, Finland, 2007.
@inproceedings{k187,
title = {Identifying saxophonists from their playing styles},
author = {A. Pertusa and E. Maestre and R. Ramírez},
year = {2007},
date = {2007-01-01},
urldate = {2007-01-01},
booktitle = {Proc. of the 30th Audio Engineering Society (AES) Conference},
address = {Saariselkä, Finland},
keywords = {PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
Pertusa, A.; Maestre, E.; Ramírez, R.
Performance-based Interpreter Identification in Saxophone Audio Recordings Journal Article
In: IEEE Transactions on Circuits and Systems for Video Technology, vol. 7, no. 3, pp. 356-364, 2007.
@article{k194,
title = {Performance-based Interpreter Identification in Saxophone Audio Recordings},
author = {A. Pertusa and E. Maestre and R. Ramírez},
year = {2007},
date = {2007-01-01},
urldate = {2007-01-01},
journal = {IEEE Transactions on Circuits and Systems for Video Technology},
volume = {7},
number = {3},
pages = {356-364},
keywords = {PROSEMUS},
pubstate = {published},
tppubtype = {article}
}
Pertusa, A.; Rauber, A.; Lidy, T.; Iñesta, J. M.
Improving genre classification by combination of audio and symbolic descriptors using a transcription system Proceedings Article
In: Proc. of the 8th Int. Conf. on Music Information Retrieval, ISMIR 2007, pp. 61-66, Vienna, Austria, 2007.
Links | BibTeX | Tags: Acc. Int. E-A, PROSEMUS
@inproceedings{k196,
title = {Improving genre classification by combination of audio and symbolic descriptors using a transcription system},
author = {A. Pertusa and A. Rauber and T. Lidy and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/196/aptl_ismir07.pdf},
year = {2007},
date = {2007-01-01},
urldate = {2007-01-01},
booktitle = {Proc. of the 8th Int. Conf. on Music Information Retrieval, ISMIR 2007},
pages = {61-66},
address = {Vienna, Austria},
keywords = {Acc. Int. E-A, PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
Iñesta, J. M.; Rizo, D.; Illescas, P. R.
Harmonic, melodic, and functional automatic analysis Proceedings Article
In: Proceedings of the 2007 International Computer Music Conferrence, pp. 165–168, 2007, ISBN: 2223-3881.
Abstract | Links | BibTeX | Tags: GV-JRRJ, PROSEMUS
@inproceedings{k201,
title = {Harmonic, melodic, and functional automatic analysis},
author = {J. M. Iñesta and D. Rizo and P. R. Illescas},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/201/icmc07_tonal_analysis.pdf},
isbn = {2223-3881},
year = {2007},
date = {2007-01-01},
urldate = {2007-01-01},
booktitle = {Proceedings of the 2007 International Computer Music Conferrence},
pages = {165--168},
abstract = {This work is an effort towards the development of a system for the automation of
traditional harmonic analysis of polyphonic scores in symbolic format. A number of stages have been
designed in this procedure: melodic analysis of harmonic and non-harmonic tones, vertical harmonic analysis, tonality, and tonal functions. All these informations are
represented as a weighted directed acyclic graph. The best possible analysis is
the path that maximizes the sum of weights in the graph, obtained through a
dynamic programming algorithm. The feasibility of this approach has been tested on six J.S. Bach's harmonized chorales},
keywords = {GV-JRRJ, PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
This work is an effort towards the development of a system for the automation of
traditional harmonic analysis of polyphonic scores in symbolic format. A number of stages have been
designed in this procedure: melodic analysis of harmonic and non-harmonic tones, vertical harmonic analysis, tonality, and tonal functions. All these informations are
represented as a weighted directed acyclic graph. The best possible analysis is
the path that maximizes the sum of weights in the graph, obtained through a
dynamic programming algorithm. The feasibility of this approach has been tested on six J.S. Bach's harmonized chorales
traditional harmonic analysis of polyphonic scores in symbolic format. A number of stages have been
designed in this procedure: melodic analysis of harmonic and non-harmonic tones, vertical harmonic analysis, tonality, and tonal functions. All these informations are
represented as a weighted directed acyclic graph. The best possible analysis is
the path that maximizes the sum of weights in the graph, obtained through a
dynamic programming algorithm. The feasibility of this approach has been tested on six J.S. Bach's harmonized chorales