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Record W2802698441 · doi:10.1007/978-3-319-58316-7_10

Design of Vibrotactile Feedback and Stimulation for Music Performance

2018· book-chapter· en· W2802698441 on OpenAlexaff
M. Giordano, John Sullivan, Marcelo M. Wanderley

Bibliographic record

VenueSpringer series on touch and haptic systems · 2018
Typebook-chapter
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsCentre for Interdisciplinary Research in Music Media and TechnologyMcGill University
Fundersnot available
KeywordsHaptic technologyHuman–computer interactionComputer scienceWearable computerCategorizationMusicalContext (archaeology)Interface (matter)MultimediaSimulationArtificial intelligenceVisual arts

Abstract

fetched live from OpenAlex

Haptics, and specifically vibrotactile-augmented interfaces, have been the object of much research in the music technology domain: In the last few decades, many musical haptic interfaces have been designed and used to teach, perform, and compose music. The investigation of the design of meaningful ways to convey musical information via the sense of touch is a paramount step toward achieving truly transparent haptic-augmented interfaces for music performance and practice, and in this chapter we present our recent work in this context. We start by defining a model for haptic-augmented interfaces for music, and a taxonomy of vibrotactile feedback and stimulation, which we use to categorize a brief literature review on the topic. We then present the design and evaluation of a haptic language of cues in the form of tactile icons delivered via vibrotactile-equipped wearable garments. This language constitutes the base of a “wearable score” used in music performance and practice. We provide design guidelines for our tactile icons and user-based evaluations to assess their effectiveness in delivering musical information and report on the system’s implementation in a live musical performance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.601
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.067
GPT teacher head0.251
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueSpringer series on touch and haptic systemsSame topicTactile and Sensory InteractionsFrench-language works237,207