Welcome to Musically Embodied Machine Learning (2025)
Description:
What are the creative possibilities of machine learning when embedded within musical instruments? If an instrument can learn and adapt with its player, how might this change musical practices? How do we adapt ML technology so that it can learn in-situ within an instrument? How do we design simplified interfaces to machine learning that become part of a musical instrument? These are the sorts of questions being explored by the Musically Embodied Machine Learning project. We’ll be bringing our new embedded ML technology to AAU for a workshop, where you can learn about embedded musical ML hands-on by building new prototype musical instruments.
The workshop will take place in Manufacturet and the Augmented Instruments Lab.
Schedule:
Day 1:
- introductions
- tutorials on concepts in musically embodied machine learning
- group prototyping and planning
Day 2:
- group work on prototyping musical instruments
Day 3:
- more prototyping
- demos, reflection and discussion
- evening: concert (with optional demo-performances)
Prerequisites:
We’ll try and accommodate a mixture of people with varied musical and technical skills. Work is in small groups. As long as skills balance out in the groups, participants will need at least one of these skills:
- Basic electronics / coding
- Musical instrument design (software and/or hardware)
- Musical performance
Learning objectives:
Understand the motivations behind the need for in-situ learning within hybrid and digital musical instruments
Understand the musically creative possibilities of embedded machine learning
Understand techniques and technologies for embedded machine learning
Develop musical prototypes for interacting with embedded machine learning in instruments
Experiment with advanced algorithms (such as extensions to reinforcement learning) that are specialised for in-situ machine learning
Carry out musical instrument prototyping in small groups
Perform with or demonstrate new musical prototypes
Key literature: TBA
Organizer: Daniel Overholt
Lecturers: Dr. Chris Kiefer, Andrea Martelloni & Daniel Overholt
ECTS: 3.0
Time: 15 - 17 January 2025
Place: Aalborg University (Room TBA)
Zip code: 9220
City: Aalborg
Maximal number of participants: 20
Deadline: 2 January 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
Description:
What are the creative possibilities of machine learning when embedded within musical instruments? If an instrument can learn and adapt with its player, how might this change musical practices? How do we adapt ML technology so that it can learn in-situ within an instrument? How do we design simplified interfaces to machine learning that become part of a musical instrument? These are the sorts of questions being explored by the Musically Embodied Machine Learning project. We’ll be bringing our new embedded ML technology to AAU for a workshop, where you can learn about embedded musical ML hands-on by building new prototype musical instruments.
The workshop will take place in Manufacturet and the Augmented Instruments Lab.
Schedule:
Day 1:
- introductions
- tutorials on concepts in musically embodied machine learning
- group prototyping and planning
Day 2:
- group work on prototyping musical instruments
Day 3:
- more prototyping
- demos, reflection and discussion
- evening: concert (with optional demo-performances)
Prerequisites:
We’ll try and accommodate a mixture of people with varied musical and technical skills. Work is in small groups. As long as skills balance out in the groups, participants will need at least one of these skills:
- Basic electronics / coding
- Musical instrument design (software and/or hardware)
- Musical performance
Learning objectives:
Understand the motivations behind the need for in-situ learning within hybrid and digital musical instruments
Understand the musically creative possibilities of embedded machine learning
Understand techniques and technologies for embedded machine learning
Develop musical prototypes for interacting with embedded machine learning in instruments
Experiment with advanced algorithms (such as extensions to reinforcement learning) that are specialised for in-situ machine learning
Carry out musical instrument prototyping in small groups
Perform with or demonstrate new musical prototypes
Key literature: TBA
Organizer: Daniel Overholt
Lecturers: Dr. Chris Kiefer, Andrea Martelloni & Daniel Overholt
ECTS: 3.0
Time: 15 - 17 January 2025
Place: Aalborg University (Room TBA)
Zip code: 9220
City: Aalborg
Maximal number of participants: 20
Deadline: 2 January 2025
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Daniel Overholt