Systems for Interactive Control of Computer Generated Music Performance

Fabiani, Marco; Friberg, Anders; Bresin, Roberto

doi:10.1007/978-1-4471-4123-5_2

Cited by 6 publications

(8 citation statements)

References 46 publications

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“…2) Synthesized Music: Real-time synthesis approaches make it easy to exert control over a larger set of musical parameters and more easily craft more complex DMIs [49]. Sonification parameters used in these designs include musical pitch [50]- [52], tempo [36], brightness [51], mix balance [51], chord arpeggio characteristics [53], musical layer richness [54], synthetic tone additions [54], and percussive sample triggering [55].…”

Section: Musical Biofeedbackmentioning

confidence: 99%

A Technical Framework for Musical Biofeedback in Stroke Rehabilitation

Kantan

Spaich

Dahl

2022

IEEE Trans. Human-Mach. Syst.

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In this article, we present a technical framework aimed at facilitating musical biofeedback research in poststroke movement rehabilitation. The framework comprises wireless wearable inertial sensors and software built with inexpensive and opensource tools. The software enables layered and adjustable music synthesis and has a generic movement-music mapping module.Using this, we designed digital musical interactions for balance, sit-to-stand, and gait training. Preliminary trials with subacute stroke patients indicated that the interactions were clinically feasible. Expert interviews with a focus group of clinicians were also conducted, where these interactions were deemed as meaningful and relevant to clinical protocols, with comprehensible feedback (albeit sometimes unpleasant or disturbing) for several patient types. We carried out system benchmarking, finding that the system has sufficiently short loop delays (∼90 ms) and a healthy sensing range (>9 m) and is computationally efficient (11.1% peak CPU usage on a quad-core processor). Future studies will focus on using this framework with patients to both develop the interactions further and measure their effects on motor learning, performance retention, and psychological factors to help gauge their true clinical potential.

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Section: Musical Biofeedbackmentioning

confidence: 99%

A Technical Framework for Musical Biofeedback in Stroke Rehabilitation

Kantan

Spaich

Dahl

2022

IEEE Trans. Human-Mach. Syst.

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“…Table 3 shows several feedback and conductor models. For a more thorough review of feedback models we refer the reader to Fabiani et al (2013). Common ways for a user to control certain aspects of a performance are either via high-level semantic descriptors that describe the intended expressive character-often selected from some 2D space related to Russell (1980)'s valence-arousal plane (Friberg, 2006;Canazza et al, 2015); or via physical gestures, measured either through motion capture (Fabiani, 2011) or by using physical interfaces (Chew et al, 2005;Dixon et al, 2005;Baba et al, 2010).…”

Section: Conductor Systemsmentioning

confidence: 99%

Computational Models of Expressive Music Performance: A Comprehensive and Critical Review

Cancino-Chacón

Grachten

Goebl

et al. 2018

Front. Digit. Humanit.

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Expressive performance is an indispensable part of music making. When playing a piece, expert performers shape various parameters (tempo, timing, dynamics, intonation, articulation, etc.) in ways that are not prescribed by the notated score, in this way producing an expressive rendition that brings out dramatic, affective, and emotional qualities that may engage and affect the listeners. Given the central importance of this skill for many kinds of music, expressive performance has become an important research topic for disciplines like musicology, music psychology, etc. This paper focuses on a specific thread of research: work on computational music performance models. Computational models are attempts at codifying hypotheses about expressive performance in terms of mathematical formulas or computer programs, so that they can be evaluated in systematic and quantitative ways. Such models can serve at least two purposes: they permit us to systematically study certain hypotheses regarding performance; and they can be used as tools to generate automated or semi-automated performances, in artistic or educational contexts. The present article presents an up-to-date overview of the state of the art in this domain. We explore recent trends in the field, such as a strong focus on data-driven (machine learning) approaches; a growing interest in interactive expressive systems, such as conductor simulators and automatic accompaniment systems; and an increased interest in exploring cognitively plausible features and models. We provide an in-depth discussion of several important design choices in such computer models, and discuss a crucial (and still largely unsolved) problem that is hindering systematic progress: the question of how to evaluate such models in scientifically and musically meaningful ways. From all this, we finally derive some research directions that should be pursued with priority, in order to advance the field and our understanding of expressive music performance.

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“…Further in this text we elaborate on musical gesture, redefining it as a concept beyond bodily movement. Previous to that we build on the latter three parts of Wanderley's proposal, which have been often presented as an electronic musical instrument's main constituting modules (Wessel & Wright, 2002;Hunt, Kirk & Neighbour, 2004;Armstrong, 2006;Magnusson, 2010;Fabiani, Friberg & Bresin, 2013;de Campo, 2014).…”

Section: Generalized Model Of Musical Machinesmentioning

confidence: 99%

“…As the interaction within a gestural agency system demands action to be taken in order to reach a musical goal, agents are necessarily presented with challenges. It has been argued that a musical instrument needs to pose a balanced challenge that is neither too much to be frustrating nor too little to be unappealing in order to be interesting (Wanderley & Orio, 2002;Wessel & Wright, 2002;Levitin, McAdams & Adams, 2002;McDermott et al, 2013;Fabiani, Friberg & Bresin, 2013). This notion conforms to the concept of flow, defined as an optimal state of wellbeing achieved by an activity that provides challenges or opportunities for action accommodating the skill level of an individual (Nakamura & Csikszentmihalyi, 2014).…”

Section: Gestural Agencymentioning

confidence: 99%

Gestural Agency in Human–Machine Musical Interaction

Mendoza¹,

Thompson²

2017

The Routledge Companion to Embodied Music Interaction

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Musical technologies are evolving in such a way that they start to resemble the people that use them. In this vision it seems pertinent to abandon the conception of musicians as users of musical instruments in favor of machines and humans interacting to make music. Both the machine and the human can be modeled as embodied cognitive agents that comprise a network of musical gestures. These gestures are multi-modal signals that allow the agents to exert influence upon each other towards making music. This standpoint integrates traditional and novel music-making technologies, towards a better understanding of musical interaction.

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Systems for Interactive Control of Computer Generated Music Performance

Cited by 6 publications

References 46 publications

A Technical Framework for Musical Biofeedback in Stroke Rehabilitation

A Technical Framework for Musical Biofeedback in Stroke Rehabilitation

Computational Models of Expressive Music Performance: A Comprehensive and Critical Review

Gestural Agency in Human–Machine Musical Interaction

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