论文标题
音乐手套仪器的机器学习
Machine Learning for a Music Glove Instrument
论文作者
论文摘要
配备有力敏感，Flex和IMU传感器的音乐手套仪器在电钢琴上训练，以根据传感器输入的时间序列学习音符序列。一旦训练，手套就会在任何表面上使用，以生成与手动运动最密切相关的音符序列。数据是由戴着手套并在电动键盘上播放的表演者手动收集的。该功能空间旨在说明关键手动运动，例如移动的拇指。使用Logistic回归以及贝叶斯信念网络学习从一个音符到另一个音符的过渡概率。这项工作总体上展示了数字乐器的数据驱动方法。
A music glove instrument equipped with force sensitive, flex and IMU sensors is trained on an electric piano to learn note sequences based on a time series of sensor inputs. Once trained, the glove is used on any surface to generate the sequence of notes most closely related to the hand motion. The data is collected manually by a performer wearing the glove and playing on an electric keyboard. The feature space is designed to account for the key hand motion, such as the thumb-under movement. Logistic regression along with bayesian belief networks are used learn the transition probabilities from one note to another. This work demonstrates a data-driven approach for digital musical instruments in general.
