Intelligent assistant for music practice

Generally, the present disclosure is directed to techniques to automatically provide feedback and suggestions to musicians. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to provide real-time feedback to musicians based on audio and/or video of the musician playing music. The techniques of this disclosure use various input features, e.g., the musician’s practice piece; references from a database of musical scores, data from different sensors, e.g., microphones, cameras, etc. to analyze the musician’s playing and provide real-time feedback or suggestions for corrections to be made, e.g., changing the tempo, playing a sharp or flat note (acting as an intelligent tuner), suggestions of practice pieces, etc

Toward Learning to Solve Insertion Tasks: A Developmental Approach Using Exploratory Behaviors and Proprioception

This paper describes an approach to solving insertion tasks by a robot that uses exploratory behaviors and proprioceptive feedback. The approach was inspired by the developmental progression of insertion abilities in both chimpanzees and humans (Hayashi et al. 2006). Before mastering insertions, the infants of the two species undergo a stage where they only press objects against other objects without releasing them. Our goal was to emulate this developmental stage on a robot to see if it may lead to simpler representations for insertion tasks. Experiments were performed using a shapesorter puzzle with three different blocks and holes

Towards an in vivo biologically inspired nanofactory

Nanotechnology is having a major impact on medicine and the treatment of disease, notably in imaging and targeted drug delivery. It may, however, be possible to go even further and design 'pseudo-cell' nanofactories that work with molecules already in the body to fight disease