Linear Basis Models for Prediction and Analysis of Musical Expression

Grachten, Maarten; Widmer, Gerhard

doi:10.1080/09298215.2012.731071

Cited by 29 publications

(36 citation statements)

References 15 publications

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“…Prior work based on the Magaloff/Chopin data has revealed that a major part of the variance explained by the LBM is accounted for by the basis functions that represent dynamic markings and pitch, respectively, whereas other basis functions had very little effect on the predictive accuracy of the model (Grachten and Widmer 2012). To gain a better insight into the role that different basis functions play in each of the models, the learned models must be studied in more detail.…”

Section: Predictive Accuracymentioning

confidence: 99%

“…On the other hand, computational models of expressive music performance attempt to investigate the relationships between certain properties of the musical score and performance context with the actual performance of the score (Widmer and Goebl 2004). These models can serve mainly analytical purposes (Widmer 2002;Windsor and Clarke 1997), mainly generative purposes (Teramura et al 2008), or both (Grindlay and Helmbold 2006;De Poli et al 2001;Grachten and Widmer 2012).…”

Section: Related Workmentioning

confidence: 99%

“…We consider a musical score a sequence of elements (Grachten and Widmer 2012). These elements include note elements (e.g.…”

Section: Modeling Expressive Dynamics In Solo Piano Performancesmentioning

confidence: 99%

“…Given a set of such basis functions, each representing a different aspect of the score, the intensity of notes in an expressive performance is modeled simply as a linear combination of the basis functions. The resulting model has been used for both predictive and analytical purposes (Grachten and Widmer 2012;.…”

Section: Introductionmentioning

confidence: 99%

“…A computational framework has been proposed in Grachten and Widmer (2012), to model the effect of structural aspects of a musical score on expressive performances of that score, in particular expressive dynamics (the relative intensity with which the notes are performed). This framework, referred to as the basis function modeling (BM) approach, follows the machine learning paradigm in that it estimates the parameters of a model from a set of recorded music performances, for which expressive parameters such as local loudness, tempo, or articulation, can be measured or computed.…”

Section: Introductionmentioning

confidence: 99%

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An evaluation of linear and non-linear models of expressive dynamics in classical piano and symphonic music

et al. 2017

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Expressive interpretation forms an important but complex aspect of music, particularly in Western classical music. Modeling the relation between musical expression and structural aspects of the score being performed is an ongoing line of research. Prior work has shown that some simple numerical descriptors of the score (capturing dynamics annotations and pitch) are effective for predicting expressive dynamics in classical piano performances. Nevertheless, the features have only been tested in a very simple linear regression model. In this work, we explore the potential of non-linear and temporal modeling of expressive dynamics. Using a set of descriptors that capture different types of structure in the musical score, we compare linear and different non-linear models in a large-scale evaluation on three different corpora, involving both piano and orchestral music. To the best of our knowledge, this is the first study where models of musical expression are evaluated on both types of music. We show that, in addition to being more accurate, non-linear models describe interactions between numerical descriptors that linear models do not.

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Section: Predictive Accuracymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…We consider a musical score a sequence of elements (Grachten and Widmer 2012). These elements include note elements (e.g.…”

Section: Modeling Expressive Dynamics In Solo Piano Performancesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An evaluation of linear and non-linear models of expressive dynamics in classical piano and symphonic music

et al. 2017

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Shaping the Music Perception of an Automatic Music Composition: An Empirical Approach for Modelling Music Expressiveness

Ventura¹

2019

Proceedings of the Tenth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018)

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Analysis of Musical Dynamics in Vocal Performances

Narang

Miron

Lizarraga

et al. 2023

Music in the AI Era

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Dynamics are one of the fundamental tools of expressivity in a performance. While the usage of this tool is highly subjective, a systematic methodology to derive loudness markings based on a performance can be highly beneficial. With this goal in mind, this paper is a first step towards developing a methodology to automatically transcribe dynamic markings from vocal rock and pop performances. To this end, we make use of commercial recordings of some popular songs followed by source separation and compare them to the karaoke versions of the same songs. The dynamic variations in the original commercial recordings are found to be structurally very similar to the aligned karaoke/multi-track versions of the same tracks. We compare and show the differences between tracks using statistical analysis, with an eventual goal to use the transcribed markings as guiding tools, to help students adapt with a specific interpretation of a given piece of music. We perform a qualitative analysis of the proposed methodology with the teachers in terms of informativeness and accuracy.

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Linear Basis Models for Prediction and Analysis of Musical Expression

Cited by 29 publications

References 15 publications

An evaluation of linear and non-linear models of expressive dynamics in classical piano and symphonic music

An evaluation of linear and non-linear models of expressive dynamics in classical piano and symphonic music

Shaping the Music Perception of an Automatic Music Composition: An Empirical Approach for Modelling Music Expressiveness

Analysis of Musical Dynamics in Vocal Performances

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