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

Entropy

Richard Allmendinger¹  Chris Rhodes²  Ricardo Climent² 

[1] Alliance Manchester Business School, University of Manchester, Manchester M15 6PB, UK;NOVARS Research Centre, University of Manchester, Manchester M13 9PL, UK;

关键词: interactive machine learning;    Wekinator;    Myo;    HCI;    performance gestures;    interactive music;   

DOI  :  10.3390/e22121384

来源: DOAJ

【 摘 要 】

Interactive music uses wearable sensors (i.e., gestural interfaces—GIs) and biometric datasets to reinvent traditional human–computer interaction and enhance music composition. In recent years, machine learning (ML) has been important for the artform. This is because ML helps process complex biometric datasets from GIs when predicting musical actions (termed performance gestures). ML allows musicians to create novel interactions with digital media. Wekinator is a popular ML software amongst artists, allowing users to train models through demonstration. It is built on the Waikato Environment for Knowledge Analysis (WEKA) framework, which is used to build supervised predictive models. Previous research has used biometric data from GIs to train specific ML models. However, previous research does not inform optimum ML model choice, within music, or compare model performance. Wekinator offers several ML models. Thus, we used Wekinator and the Myo armband GI and study three performance gestures for piano practice to solve this problem. Using these, we trained all models in Wekinator and investigated their accuracy, how gesture representation affects model accuracy and if optimisation can arise. Results show that neural networks are the strongest continuous classifiers, mapping behaviour differs amongst continuous models, optimisation can occur and gesture representation disparately affects model mapping behaviour; impacting music practice.

【 授权许可】

Unknown