What Were You Expecting? Using Expectancy Features to Predict Expressive Performances of Classical Piano Music

Carlos Eduardo Cancino Chacon; Maarten Grachten; David Sears; Gerhard Widmer

What Were You Expecting? Using Expectancy Features to Predict Expressive Performances of Classical Piano Music

Carlos Eduardo Cancino Chacon
, Maarten Grachten
, David Sears
, Gerhard Widmer

Institute of Computational Perception

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

In this paper we present preliminary work examining the relationship between the formation of expectations and the realization of musical performances, paying particular attention to expressive tempo and dynamics. To compute features that re ect what a listener is expecting to hear, we employ a computational model of auditory expectation called the Information Dynamics of Music model (IDyOM). We then explore how well these expectancy features { when combined with score descriptors using the Basis-Function modeling approach { can predict expressive tempo and dynamics in a dataset of Mozart piano sonata performances. Our results suggest that using expectancy features significantly improves the predictions for tempo.

Original language	English
Title of host publication	Proceedings of the 10th International Workshop on Machine Learning and Music (MML 2017)
Number of pages	6
Publication status	Published - Oct 2017

Fields of science

202002 Audiovisual media
102 Computer Sciences
102001 Artificial intelligence
102003 Image processing
102015 Information systems

JKU Focus areas

Computation in Informatics and Mathematics
Engineering and Natural Sciences (in general)

Cite this

@inproceedings{4502421866214a5da3a14e08c2fb4dfb,

title = "What Were You Expecting? Using Expectancy Features to Predict Expressive Performances of Classical Piano Music",

abstract = "In this paper we present preliminary work examining the relationship between the formation of expectations and the realization of musical performances, paying particular attention to expressive tempo and dynamics. To compute features that re ect what a listener is expecting to hear, we employ a computational model of auditory expectation called the Information Dynamics of Music model (IDyOM). We then explore how well these expectancy features \{ when combined with score descriptors using the Basis-Function modeling approach \{ can predict expressive tempo and dynamics in a dataset of Mozart piano sonata performances. Our results suggest that using expectancy features significantly improves the predictions for tempo.",

author = "\{Cancino Chacon\}, \{Carlos Eduardo\} and Maarten Grachten and David Sears and Gerhard Widmer",

year = "2017",

month = oct,

language = "English",

booktitle = "Proceedings of the 10th International Workshop on Machine Learning and Music (MML 2017)",

}

What Were You Expecting? Using Expectancy Features to Predict Expressive Performances of Classical Piano Music. / Cancino Chacon, Carlos Eduardo; Grachten, Maarten; Sears, David et al.
Proceedings of the 10th International Workshop on Machine Learning and Music (MML 2017). 2017.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

TY - GEN

T1 - What Were You Expecting? Using Expectancy Features to Predict Expressive Performances of Classical Piano Music

AU - Cancino Chacon, Carlos Eduardo

AU - Grachten, Maarten

AU - Sears, David

AU - Widmer, Gerhard

PY - 2017/10

Y1 - 2017/10

N2 - In this paper we present preliminary work examining the relationship between the formation of expectations and the realization of musical performances, paying particular attention to expressive tempo and dynamics. To compute features that re ect what a listener is expecting to hear, we employ a computational model of auditory expectation called the Information Dynamics of Music model (IDyOM). We then explore how well these expectancy features { when combined with score descriptors using the Basis-Function modeling approach { can predict expressive tempo and dynamics in a dataset of Mozart piano sonata performances. Our results suggest that using expectancy features significantly improves the predictions for tempo.

AB - In this paper we present preliminary work examining the relationship between the formation of expectations and the realization of musical performances, paying particular attention to expressive tempo and dynamics. To compute features that re ect what a listener is expecting to hear, we employ a computational model of auditory expectation called the Information Dynamics of Music model (IDyOM). We then explore how well these expectancy features { when combined with score descriptors using the Basis-Function modeling approach { can predict expressive tempo and dynamics in a dataset of Mozart piano sonata performances. Our results suggest that using expectancy features significantly improves the predictions for tempo.

M3 - Conference proceedings

BT - Proceedings of the 10th International Workshop on Machine Learning and Music (MML 2017)

ER -