Computational Modeling of Music Cognition: A Case Study on Model Selection

Honing, Henkjan

doi:10.1525/mp.2006.23.5.365

Cited by 29 publications

(30 citation statements)

References 28 publications

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“…However, our main focus here is not a general psychological validation of these models. By measuring the fit between the model and the empirical observation we provide the starting point for a more general verification of these models, as pointed out by Honing (2006). Moreover, we suggest specific model selection criteria that are important within the context of MIR.…”

Section: Model Selectionmentioning

confidence: 85%

“…However, his system is only applicable to symbolic metrical models and hence not applicable to the audio-based models discussed by Gouyon and Dixon (2005). Honing (2006) on the contrary states that in the specific case of a computational model in music cognition the goodness of fit with the empirical data is not sufficient in order to test the validity of a model. Hence, the number of correct answers of the system should not be the only criterion of an evaluation system.…”

Section: Model Selectionmentioning

confidence: 99%

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Cognition-based Segmentation for Music Information Retrieval Systems

Wiering

Nooijer²,

Volk

et al. 2009

Journal of New Music Research

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Background in computer science. This paper investigates the generic problem of model selection in the specific context of Music Information Retrieval (MIR). In MIR research, similarity measures are developed for ranking musical items with respect to their relevance to a user's musical query. The application of such similarity measures in MIR systems typically requires musical works to be divided into more manageable units. This involves two tasks: melody segmentation and voice separation. For both of these tasks, several solutions have been proposed in the symbolic domain. It seems reasonable to assume that those solutions that are most in accordance with human performance will result in the best ranking of retrieval output. As a first step towards this goal, this paper describes the evaluation of ten prominent methods against human performance. Background in cognition.Human listeners generally possess two functions that allow them to process a continuous stream of music into understandable units: the ability to perceive multiple, successive tones as one coherent melodic phrase (melody segmentation) and the ability to differentiate melody notes from harmony notes (voice separation). Algorithms for mimicking these human functions have been developed from two perspectives: model-driven, taking Gestalt principles as a starting-point; and data-driven, inferring rules by learning from large amounts of data. One method excepted, this research focuses on model-driven approaches.

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Section: Model Selectionmentioning

confidence: 85%

Section: Model Selectionmentioning

confidence: 99%

Cognition-based Segmentation for Music Information Retrieval Systems

Wiering

Nooijer²,

Volk

et al. 2009

Journal of New Music Research

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show abstract

“…While a final ritard might coarsely resemble a square root function (according to a kinematic model), the predictions made by perception-based models are also influenced by the perceived temporal structure of the musical material that constraints possible shapes of the ritard. It might therefore be considered a potentially stronger theory than one that only makes a good fit (Roberts & Pashler, 2000;Honing, 2004). However, the theoretical predictions made by the combination of a quantization and tempo track model still must be informed by a systematic empirical study to see how precisely the structural and temporal factors mentioned constrain a musical performance.…”

Section: Resultsmentioning

confidence: 99%

IS THERE A PERCEPTION-BASED ALTERNATIVE TO KINEMATIC MODELS OF TEMPO RUBATO?

Honing

2005

Music Perception

Self Cite

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THE RELATION BETWEEN MUSIC and motion has been a topic of much theoretical and empirical research. An important contribution is made by a family of computational theories, so-called kinematic models, that propose an explicit relation between the laws of physical motion in the real world and expressive timing in music performance. However, kinematic models predict that expressive timing is independent of (a) the number of events, (b) the rhythmic structure, and (c) the overall tempo of the performance. These factors have no effect on the predicted shape of a ritardando. Computer simulations of a number of rhythm perception models show, however, a large effect of these structural and temporal factors. They are therefore proposed as a perception-based alternative to the kinematic approach.Received February 25, 2004, accepted June 26, 2004 T HE RELATION BETWEEN MUSIC and motion has been studied in a large body of theoretical and empirical work. (See Shove and Repp [1995] for a partial overview.) However, it is very difficult to specify-let alone validate-what is the nature of this longassumed relationship (Clarke, 2001;Honing, 2003). An important contribution to this topic is made by a family of computational theories, so-called kinematic models, that propose an explicit relation between the laws of physical motion (elementary mechanics) in the real world and expressive timing in music performance (Sundberg & Verillo, 1980;Kronman & Sundberg, 1987;Longuet-Higgins & Lisle, 1989;Feldman, Epstein, & Richards 1992;Todd, 1992;Epstein, 1994;Todd, 1995;Friberg & Sundberg, 1999). A large number of these theories focus on modeling the final ritard, the typical slowing down at the end of a music performance, especially in music from the Western Baroque and Romantic periods. But this characteristic slowing down can also be observed in, for instance, Javanese gamelan music or some pop and jazz genres. These models were

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“…In the computer vision and image processing communities there have been several efforts in this direction, including the definition of the structural similarity index (Wang et al 2004). For musical expression, a starting point is the perceptual model described in Honing (2006).…”

Section: Discussionmentioning

confidence: 99%

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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Computational Modeling of Music Cognition: A Case Study on Model Selection

Cited by 29 publications

References 28 publications

Cognition-based Segmentation for Music Information Retrieval Systems

Cognition-based Segmentation for Music Information Retrieval Systems

IS THERE A PERCEPTION-BASED ALTERNATIVE TO KINEMATIC MODELS OF TEMPO RUBATO?

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

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