The Experts below are selected from a list of 18633 Experts worldwide ranked by ideXlab platform
José M. Iñesta - One of the best experts on this subject based on the ideXlab platform.
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SSPR/SPR - A shallow description framework for Musical style recognition
Lecture Notes in Computer Science, 2004Co-Authors: Pedro J. Ponce De León, Carlos Pérez-sancho, José M. IñestaAbstract:In the field of computer music, pattern recognition algorithms are very relevant for music information retrieval (MIR). One challenging task within this area is the automatic recognition of Musical style, that has a number of applications like indexing and selecting Musical databases. In this paper, the classification of monophonic melodies of two different Musical Styles (jazz and classical) represented symbolically as MIDI files is studied, using different classification methods: Bayesian classifier and nearest neighbour classifier. From the music sequences, a number of melodic, harmonic, and rhythmic statistical descriptors are computed and used for style recognition. We present a performance analysis of such algorithms against different description models and parameters.
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Musical style classification from symbolic data: A two-Styles case study
Lecture Notes in Computer Science, 2004Co-Authors: Pedro J. Ponce De León, José M. IñestaAbstract:In this paper the classification of monophonic melodies from two different Musical Styles (Jazz and classical) is studied using different classification methods: Bayesian classifier, a k-NN classifier, and self-organising maps (SOM). From MIDI files, the monophonic melody track is extracted and cut into fragments of equal length. From these sequences, A number of melodic, harmonic, and rhythmic numerical descriptors are computed and analysed in terms of separability in two music classes, obtaining several reduced descriptor sets. Finally, the classification results for each type of classifier for the different descriptor models are compared. This scheme has a number of applications like indexing and selecting Musical databases or the evaluation of style-specific automatic composition systems.
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CMMR - Musical Style Classification from Symbolic Data: A Two-Styles Case Study
Computer Music Modeling and Retrieval, 2004Co-Authors: Pedro J. Ponce De León, José M. IñestaAbstract:In this paper the classification of monophonic melodies from two different Musical Styles (Jazz and classical) is studied using different classification methods: Bayesian classifier, a k-NN classifier, and self-organising maps (SOM). From MIDI files, the monophonic melody track is extracted and cut into fragments of equal length. From these sequences, A number of melodic, harmonic, and rhythmic numerical descriptors are computed and analysed in terms of separability in two music classes, obtaining several reduced descriptor sets. Finally, the classification results for each type of classifier for the different descriptor models are compared. This scheme has a number of applications like indexing and selecting Musical databases or the evaluation of style-specific automatic composition systems.
Frédéric Tantini - One of the best experts on this subject based on the ideXlab platform.
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Learning Stochastic Finite Automata for Musical Style Recognition
Lecture Notes in Computer Science, 2006Co-Authors: Colin De La Higuera, Frédéric Piat, Frédéric TantiniAbstract:We use stochastic deterministic finite automata to model Musical Styles: a same automaton can be used to classify new melodies but also to generate them. Through grammatical inference these automata are learned and new pieces of music can be parsed. We show that this works by proposing promising classification results.
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CIAA - Learning stochastic finite automata for Musical style recognition
Implementation and Application of Automata, 2006Co-Authors: Colin De La Higuera, Frédéric Piat, Frédéric TantiniAbstract:We use stochastic deterministic finite automata to model Musical Styles: a same automaton can be used to classify new melodies but also to generate them. Through grammatical inference these automata are learned and new pieces of music can be parsed. We show that this works by proposing promising classification results.
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Learning Stochastic Finite Automata for Musical Style Recognition
2005Co-Authors: Colin De La Higuera, Frédéric Piat, Frédéric TantiniAbstract:Stochastic deterministic finite automata have been introduced and are used in a variety of settings. We use them to model Musical Styles: a same automaton can be used to classify new melodies but also to generate them. Through grammatical inference these automata are learned and new pieces of music can be parsed. We show that this works by proposing promising classification results and discuss further work.
Pedro J. Ponce De León - One of the best experts on this subject based on the ideXlab platform.
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SSPR/SPR - A shallow description framework for Musical style recognition
Lecture Notes in Computer Science, 2004Co-Authors: Pedro J. Ponce De León, Carlos Pérez-sancho, José M. IñestaAbstract:In the field of computer music, pattern recognition algorithms are very relevant for music information retrieval (MIR). One challenging task within this area is the automatic recognition of Musical style, that has a number of applications like indexing and selecting Musical databases. In this paper, the classification of monophonic melodies of two different Musical Styles (jazz and classical) represented symbolically as MIDI files is studied, using different classification methods: Bayesian classifier and nearest neighbour classifier. From the music sequences, a number of melodic, harmonic, and rhythmic statistical descriptors are computed and used for style recognition. We present a performance analysis of such algorithms against different description models and parameters.
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Musical style classification from symbolic data: A two-Styles case study
Lecture Notes in Computer Science, 2004Co-Authors: Pedro J. Ponce De León, José M. IñestaAbstract:In this paper the classification of monophonic melodies from two different Musical Styles (Jazz and classical) is studied using different classification methods: Bayesian classifier, a k-NN classifier, and self-organising maps (SOM). From MIDI files, the monophonic melody track is extracted and cut into fragments of equal length. From these sequences, A number of melodic, harmonic, and rhythmic numerical descriptors are computed and analysed in terms of separability in two music classes, obtaining several reduced descriptor sets. Finally, the classification results for each type of classifier for the different descriptor models are compared. This scheme has a number of applications like indexing and selecting Musical databases or the evaluation of style-specific automatic composition systems.
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CMMR - Musical Style Classification from Symbolic Data: A Two-Styles Case Study
Computer Music Modeling and Retrieval, 2004Co-Authors: Pedro J. Ponce De León, José M. IñestaAbstract:In this paper the classification of monophonic melodies from two different Musical Styles (Jazz and classical) is studied using different classification methods: Bayesian classifier, a k-NN classifier, and self-organising maps (SOM). From MIDI files, the monophonic melody track is extracted and cut into fragments of equal length. From these sequences, A number of melodic, harmonic, and rhythmic numerical descriptors are computed and analysed in terms of separability in two music classes, obtaining several reduced descriptor sets. Finally, the classification results for each type of classifier for the different descriptor models are compared. This scheme has a number of applications like indexing and selecting Musical databases or the evaluation of style-specific automatic composition systems.
Colin De La Higuera - One of the best experts on this subject based on the ideXlab platform.
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Learning Stochastic Finite Automata for Musical Style Recognition
Lecture Notes in Computer Science, 2006Co-Authors: Colin De La Higuera, Frédéric Piat, Frédéric TantiniAbstract:We use stochastic deterministic finite automata to model Musical Styles: a same automaton can be used to classify new melodies but also to generate them. Through grammatical inference these automata are learned and new pieces of music can be parsed. We show that this works by proposing promising classification results.
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CIAA - Learning stochastic finite automata for Musical style recognition
Implementation and Application of Automata, 2006Co-Authors: Colin De La Higuera, Frédéric Piat, Frédéric TantiniAbstract:We use stochastic deterministic finite automata to model Musical Styles: a same automaton can be used to classify new melodies but also to generate them. Through grammatical inference these automata are learned and new pieces of music can be parsed. We show that this works by proposing promising classification results.
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Learning Stochastic Finite Automata for Musical Style Recognition
2005Co-Authors: Colin De La Higuera, Frédéric Piat, Frédéric TantiniAbstract:Stochastic deterministic finite automata have been introduced and are used in a variety of settings. We use them to model Musical Styles: a same automaton can be used to classify new melodies but also to generate them. Through grammatical inference these automata are learned and new pieces of music can be parsed. We show that this works by proposing promising classification results and discuss further work.
Antonius M J Vandongen - One of the best experts on this subject based on the ideXlab platform.
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classification of Musical Styles using liquid state machines
International Joint Conference on Neural Network, 2010Co-Authors: Antonius M J VandongenAbstract:Music Information Retrieval (MIR) is an interdisciplinary field that facilitates indexing and content-based organization of music databases. Music classification and clustering is one of the major topics in MIR. Music can be defined as ‘organized sound’. The highly ordered temporal structure of music suggests it should be amendable to analysis by a novel spiking neural network paradigm: the liquid state machine (LSM). Unlike conventional statistical approaches that require the presence of static input data, the LSM has a unique ability to classify music in real-time, due to its dynamics and fading-memory. This paper investigates the performance of an LSM in classifying Musical Styles (ragtime vs. classical), as well as its ability to distinguish music from note sequences without temporal structure. The results show that the LSM performs admirably in this task.
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IJCNN - Classification of Musical Styles using liquid state machines
The 2010 International Joint Conference on Neural Networks (IJCNN), 2010Co-Authors: Antonius M J VandongenAbstract:Music Information Retrieval (MIR) is an interdisciplinary field that facilitates indexing and content-based organization of music databases. Music classification and clustering is one of the major topics in MIR. Music can be defined as ‘organized sound’. The highly ordered temporal structure of music suggests it should be amendable to analysis by a novel spiking neural network paradigm: the liquid state machine (LSM). Unlike conventional statistical approaches that require the presence of static input data, the LSM has a unique ability to classify music in real-time, due to its dynamics and fading-memory. This paper investigates the performance of an LSM in classifying Musical Styles (ragtime vs. classical), as well as its ability to distinguish music from note sequences without temporal structure. The results show that the LSM performs admirably in this task.
