Air Drums, and Bass: Anticipating Musical Gestures in Accelerometer Signals with a Lightweight CNN

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dc.contributor.authorTavares, Tiago Fernandes
dc.contributor.authorBertoloto, Lucas
dc.creatorTavares, Tiago Fernandes
dc.creatorBertoloto, Lucas
dc.date.accessioned2024-06-24T18:14:31Z
dc.date.available2024-06-24T18:14:31Z
dc.date.issued2023
dc.description.abstractDetecting gestures has often been performed using non-causal techniques such as Hidden Markov Models or pick-peaking and thresholding. They can present perceptible delay that harms their use in real-time scenarios, unless a very high sampling rate is used. In this work, we investigate a lightweight CNN-based neural network to predict and anticipate musical cues (i.e., drum hits or note onsets) from accelerometer signals. We show that our architecture is able to anticipate gestures using preparatory movements, such as raising the drumstick, thus being potentially usable in music- or gaming-related interactive devices.en
dc.formatDigital
dc.format.extentp. 1 - 5
dc.identifier.doi10.1109/MLSP55844.2023.10285920
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/6703
dc.language.isoen
dc.subjectAccelerometersen
dc.subjectPerformance evaluationen
dc.subjectNeural networksen
dc.subjectTime series analysisen
dc.subjectMusicen
dc.subjectComputer architectureen
dc.subjectReal-time systemsen
dc.titleAir Drums, and Bass: Anticipating Musical Gestures in Accelerometer Signals with a Lightweight CNN
dspace.entity.typePublication
local.description.eventIEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)
local.identifier.sourceUrihttps://ieeexplore.ieee.org/document/10285920
local.publisher.countryNão Informado
local.subject.cnpqOUTROS
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