Automatic phrase continuation from guitar and bass guitar melodies
A framework is proposed for generating interesting, musically similar variations of a given monophonic melody. The focus is on pop/rock guitar and bass guitar melodies with the aim of eventual extensions to other instruments and musical styles. It is demonstrated here how learning musical style from...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2013 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10230/56383 |
| Acceso en línea: | http://hdl.handle.net/10230/56383 http://dx.doi.org/10.1162/COMJ_a_00184 |
| Access Level: | acceso abierto |
| Palabra clave: | Composició musical Música -- Disseny assistit per ordinador |
| Sumario: | A framework is proposed for generating interesting, musically similar variations of a given monophonic melody. The focus is on pop/rock guitar and bass guitar melodies with the aim of eventual extensions to other instruments and musical styles. It is demonstrated here how learning musical style from segmented audio data can be formulated as an unsupervised learning problem to generate a symbolic representation. A melody is first segmented into a sequence of notes using onset detection and pitch estimation. A set of hierarchical, coarse-to-fine symbolic representations of the melody is generated by clustering pitch values at multiple similarity thresholds. The variance ratio criterion is then used to select the appropriate clustering levels in the hierarchy. Note onsets are aligned with beats, considering the estimated meter of the melody, to create a sequence of symbols that represent the rhythm in terms of onsets/rests and the metrical locations of their occurrence. A joint representation based on the cross-product of the pitch cluster indices and metrical locations is used to train the prediction model, a variable-length Markov chain. The melodies generated by the model were evaluated through a questionnaire by a group of experts, and received an overall positive response. |
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