Modeling and Control of Expressiveness in Music Performance

Canazza, Sergio; Poli, Giovanni De; Drioli, Carlo; Rodà, Antonio; Vidolin, Alvise

doi:10.1109/jproc.2004.825889

Cited by 47 publications

(27 citation statements)

References 33 publications

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“…Possible fields of application include all systems that interact with the user by means of music, such as automatic music description, classification, and query by content in medium-large archives and digital libraries. The authors strongly believe that the formalization and systematization of aspects related to musical affects may extend the possibility of technology-mediated interaction with music, contributing to the improvement of systems in the context of active listening [54], entertainment, and music education (see e.g. [55]).…”

Section: Discussionmentioning

confidence: 99%

Clustering Affective Qualities of Classical Music: Beyond the Valence-Arousal Plane

Rodà

Canazza

Poli

2014

IEEE Trans. Affective Comput.

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The important role of the valence and arousal dimensions in representing and recognizing affective qualities in music is well established. There is less evidence for the contribution of secondary dimensions such as potency, tension and energy. In particular, previous studies failed to find significant relations between computable musical features and affective dimensions other than valence and arousal. Here we present two experiments aiming at assessing how musical features, directly computable from complex audio excerpts, are related to secondary emotion dimensions. To this aim, we imposed some constraints on the musical features, namely modality and tempo, of the stimuli.The results show that although arousal and valence dominate for many musical features, it is possible to identify features, in particular Roughness, Loudness, and SpectralFlux, that are significantly related to the potency dimension. As far as we know, this is the first study that gained more insight into the affective potency in the music domain by using real music recordings and a computational approach

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Section: Discussionmentioning

confidence: 99%

Clustering Affective Qualities of Classical Music: Beyond the Valence-Arousal Plane

Rodà

Canazza

Poli

2014

IEEE Trans. Affective Comput.

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“…CaRo [Canazza et al 2000a[Canazza et al , 2000b[Canazza et al , 2001b[Canazza et al , 2003[Canazza et al , 2004] is a monophonic CSEMP designed to generate audio files which-like CMERS (Section 3.1.1)-express certain moods/emotions. It does not require a score to work from, but works on audio files which are mood-neutral.…”

Section: Caromentioning

confidence: 99%

A survey of computer systems for expressive music performance

Kirke

Miranda

2009

ACM Comput. Surv.

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We present a survey of research into automated and semiautomated computer systems for expressive performance of music. We will examine the motivation for such systems and then examine the majority of the systems developed over the last 25 years. To highlight some of the possible future directions for new research, the review uses primary terms of reference based on four elements: testing status, expressive representation, polyphonic ability, and performance creativity.

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“…Two previous systems have been developed that use this approach: KTH's Director Musices (Friberg 1991;Bresin and Friberg 2000a;Friberg, Bresin, and Sundberg 2006), and CaRo (Canazza et al 2004). Both of these rule-based systems were originally designed for automated expressive performance.…”

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confidence: 99%

Changing Musical Emotion: A Computational Rule System for Modifying Score and Performance

Livingstone

Muhlberger

Brown³

et al. 2010

Computer Music Journal

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Composers and performers communicate emotional intentions through the control of basic musical features such as pitch, loudness, and articulation. The extent to which emotion can be controlled by software through the systematic manipulation of these features has not been fully examined. To address this, we present CMERS, a Computational Music Emotion Rule System for the real-time control of musical emotion that modifies features at both the score level and the performance level. In Experiment 1, 20 participants continuously rated the perceived emotion of works each modified to express happy, sad, angry, tender, and normal. Intended emotion was identified correctly at 78%, with valence and arousal significantly shifted regardless of the works' original emotions. Existing systems developed for expressive performance, such as Director Musices (DM), focus on modifying features of performance. To study emotion more broadly, CMERS modifies features of both score and performance. In Experiment 2, 18 participants rated music works modified by CMERS and DM to express five emotions. CMERS's intended emotion was correctly identified at 71%, DM at 49%. CMERS achieved significant shifts in valence and arousal, DM in arousal only. These results suggest that features of the score are important for controlling valence. The effects of musical training on emotional identification accuracy are also discussed.

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Modeling and Control of Expressiveness in Music Performance

Cited by 47 publications

References 33 publications

Clustering Affective Qualities of Classical Music: Beyond the Valence-Arousal Plane

Clustering Affective Qualities of Classical Music: Beyond the Valence-Arousal Plane

A survey of computer systems for expressive music performance

Changing Musical Emotion: A Computational Rule System for Modifying Score and Performance

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