Composer Recognition based on 2D-Filtered Piano-Rolls

Gissel Velarde, Tillman Weyde, Carlos Cancino Chacón, David Meredith, Maarten Grachten

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

2 Citationer (Scopus)
99 Downloads (Pure)

Resumé

We propose a method for music classification based on the use of convolutional models on symbolic pitch-time representations (i.e. piano-rolls) which we apply to composer recognition. An excerpt of a piece to be classified is first sampled to a 2D pitch-time representation which is then subjected to various transformations, including convolution with predefined filters (Morlet or Gaussian) and classified by means of support vector machines. We combine classifiers based on different pitch representations (MIDI and morphetic pitch) and different filter types and configurations. The method does not require parsing of the music into separate voices, or extraction of any other predefined features prior to processing; instead it is based on the analysis of texture in a 2D pitch-time representation. We show that filtering significantly improves recognition and that the method proves robust to encoding, transposition and amount of information. On discriminating between Haydn and Mozart string quartet movements, our best classifier reaches state-of-the-art performance in
leave-one-out cross validation.
OriginalsprogEngelsk
TitelProceedings of the 17th International Conference on Music Information Retrieval
ForlagInternational Society for Music Information Retrieval
Publikationsdato7 aug. 2016
Udgave17
Sider115-121
ISBN (Trykt)978­0­692­75506­8, 0­692­75506­3
StatusUdgivet - 7 aug. 2016
BegivenhedInternational Society for Music Information Retrieval Conference - New York, USA
Varighed: 7 aug. 201616 aug. 2016
Konferencens nummer: 17
https://wp.nyu.edu/ismir2016/

Konference

KonferenceInternational Society for Music Information Retrieval Conference
Nummer17
LandUSA
ByNew York
Periode07/08/201616/08/2016
Internetadresse

Fingerprint

Classifiers
Convolution
Support vector machines
Textures
Processing

Citer dette

Velarde, G., Weyde, T., Cancino Chacón, C., Meredith, D., & Grachten, M. (2016). Composer Recognition based on 2D-Filtered Piano-Rolls. I Proceedings of the 17th International Conference on Music Information Retrieval (17 udg., s. 115-121). International Society for Music Information Retrieval.
Velarde, Gissel ; Weyde, Tillman ; Cancino Chacón, Carlos ; Meredith, David ; Grachten, Maarten. / Composer Recognition based on 2D-Filtered Piano-Rolls. Proceedings of the 17th International Conference on Music Information Retrieval. 17. udg. International Society for Music Information Retrieval, 2016. s. 115-121
@inproceedings{a13e0f92df7f477685403e8b9441bb9f,
title = "Composer Recognition based on 2D-Filtered Piano-Rolls",
abstract = "We propose a method for music classification based on the use of convolutional models on symbolic pitch-time representations (i.e. piano-rolls) which we apply to composer recognition. An excerpt of a piece to be classified is first sampled to a 2D pitch-time representation which is then subjected to various transformations, including convolution with predefined filters (Morlet or Gaussian) and classified by means of support vector machines. We combine classifiers based on different pitch representations (MIDI and morphetic pitch) and different filter types and configurations. The method does not require parsing of the music into separate voices, or extraction of any other predefined features prior to processing; instead it is based on the analysis of texture in a 2D pitch-time representation. We show that filtering significantly improves recognition and that the method proves robust to encoding, transposition and amount of information. On discriminating between Haydn and Mozart string quartet movements, our best classifier reaches state-of-the-art performance inleave-one-out cross validation.",
keywords = "Classification, Filtering, piano-roll, Music information retrieval, composer style",
author = "Gissel Velarde and Tillman Weyde and {Cancino Chac{\'o}n}, Carlos and David Meredith and Maarten Grachten",
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Velarde, G, Weyde, T, Cancino Chacón, C, Meredith, D & Grachten, M 2016, Composer Recognition based on 2D-Filtered Piano-Rolls. i Proceedings of the 17th International Conference on Music Information Retrieval. 17 udg, International Society for Music Information Retrieval, s. 115-121, International Society for Music Information Retrieval Conference, New York, USA, 07/08/2016.

Composer Recognition based on 2D-Filtered Piano-Rolls. / Velarde, Gissel; Weyde, Tillman; Cancino Chacón, Carlos; Meredith, David; Grachten, Maarten.

Proceedings of the 17th International Conference on Music Information Retrieval. 17. udg. International Society for Music Information Retrieval, 2016. s. 115-121.

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

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T1 - Composer Recognition based on 2D-Filtered Piano-Rolls

AU - Velarde, Gissel

AU - Weyde, Tillman

AU - Cancino Chacón, Carlos

AU - Meredith, David

AU - Grachten, Maarten

PY - 2016/8/7

Y1 - 2016/8/7

N2 - We propose a method for music classification based on the use of convolutional models on symbolic pitch-time representations (i.e. piano-rolls) which we apply to composer recognition. An excerpt of a piece to be classified is first sampled to a 2D pitch-time representation which is then subjected to various transformations, including convolution with predefined filters (Morlet or Gaussian) and classified by means of support vector machines. We combine classifiers based on different pitch representations (MIDI and morphetic pitch) and different filter types and configurations. The method does not require parsing of the music into separate voices, or extraction of any other predefined features prior to processing; instead it is based on the analysis of texture in a 2D pitch-time representation. We show that filtering significantly improves recognition and that the method proves robust to encoding, transposition and amount of information. On discriminating between Haydn and Mozart string quartet movements, our best classifier reaches state-of-the-art performance inleave-one-out cross validation.

AB - We propose a method for music classification based on the use of convolutional models on symbolic pitch-time representations (i.e. piano-rolls) which we apply to composer recognition. An excerpt of a piece to be classified is first sampled to a 2D pitch-time representation which is then subjected to various transformations, including convolution with predefined filters (Morlet or Gaussian) and classified by means of support vector machines. We combine classifiers based on different pitch representations (MIDI and morphetic pitch) and different filter types and configurations. The method does not require parsing of the music into separate voices, or extraction of any other predefined features prior to processing; instead it is based on the analysis of texture in a 2D pitch-time representation. We show that filtering significantly improves recognition and that the method proves robust to encoding, transposition and amount of information. On discriminating between Haydn and Mozart string quartet movements, our best classifier reaches state-of-the-art performance inleave-one-out cross validation.

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KW - Filtering

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KW - composer style

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Velarde G, Weyde T, Cancino Chacón C, Meredith D, Grachten M. Composer Recognition based on 2D-Filtered Piano-Rolls. I Proceedings of the 17th International Conference on Music Information Retrieval. 17 udg. International Society for Music Information Retrieval. 2016. s. 115-121