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New Interfaces and Approaches to Machine Learning When Classifying Gestures within Music.

Chris Rhodes1, Richard Allmendinger2, Ricardo Climent1

  • 1NOVARS Research Centre, University of Manchester, Manchester M13 9PL, UK.

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Machine learning models were compared for interactive music using wearable sensors. Neural networks excelled at classifying performance gestures, optimizing musical interactions.

Keywords:
HCIMyoWekinatorgestural interfacesgesture representationinteractive machine learninginteractive musicmusic compositionoptimisationperformance gestures

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Area of Science:

  • Human-computer interaction
  • Machine learning in music
  • Wearable sensor technology

Background:

  • Interactive music systems utilize wearable sensors (gestural interfaces-GIs) and biometric data to innovate human-computer interaction and music composition.
  • Machine learning (ML) is crucial for processing complex biometric datasets from GIs to predict musical actions (performance gestures), enabling novel digital media interactions for musicians.
  • Wekinator, a popular ML software built on the Waikato Environment for Knowledge Analysis (WEKA) framework, allows users to train supervised predictive models through demonstration.

Purpose of the Study:

  • To address the lack of information on optimal ML model selection and performance comparison within interactive music.
  • To investigate the accuracy of various ML models available in Wekinator using biometric data from a Myo armband GI.
  • To analyze how different gesture representations impact model accuracy and identify potential for optimization in music practice.

Main Methods:

  • Utilized Wekinator software and the Myo armband gestural interface (GI).
  • Trained all available ML models within Wekinator using three distinct performance gestures for piano practice.
  • Investigated model accuracy, the effect of gesture representation on accuracy, and the potential for optimization.

Main Results:

  • Neural networks demonstrated the strongest performance as continuous classifiers for the studied gestures.
  • Variations in mapping behavior were observed among different continuous ML models.
  • Gesture representation significantly and disparately affected model mapping behavior, impacting the potential for music practice optimization.

Conclusions:

  • The choice of ML model and gesture representation critically influences the effectiveness of interactive music systems.
  • Neural networks offer robust capabilities for real-time gesture recognition in music performance.
  • Further research into optimizing gesture representation and ML model selection can enhance the development of novel musical interactions.