MADD Sampler
A soft-surface musical sampler and editor co-created for ASCAP
Tags: Hardware, Design, Music
About the project
As part of a Fellowship with the American Society of Composers, Authors and Publishers (ASCAP), our team developed a new way of creating and interacting with sound.
Context: Electronic musical instruments are stuck
Electronic musical instruments are stuck in an old paradigm of interaction. Today, we are mostly able to press a button, toggle a switch, or turn a knob. Music evolved from expressive mechanical instruments such as the piano, yet the current offer for low-cost electronic instruments simply does not provide a compelling layer of expressivity.
As part of a collaboration between the NYC Media Lab and ASCAP, our team developed a prototype from the ground up to solve this problem. Arnab Chakravarty, Dana Elkis, Matt Ross, and I tackled the limits of expressivity by inventing the MADD Sampler: a portable soft-surface sampler and editor.
Identifying possible solutions
There are many ways in which expressive instruments can be developed and the technical aspects of such an adventure were very exciting. We focused on creating an experience around the user that would encompass them in a bubble of sound they can take anywhere.
Extensive market research taught us that contrary to our initial hypothesis, good and robust expressive instruments exist already. However, their price tags are close to thousands of dollars and they are designed for advanced and niche musicians.
The 4 guiding principles
The project was developed focusing on 4 guiding principles unveiled during the user and market research phase:
- Joyfulness
- Musicality
- Design
- University.
These were crucial in guiding our team through the abstract forms of an early prototype, and by focusing on Universality, the MADD Sampler was designed to have an incredibly low entry barrier.
Media Coverage
Our prototype and conversation with the legend Vernon Reid were covered by different media outlets. You can read about them by clicking on the respective image.
Tools
- Raspberry Pi
- Arduino
- Python
- PureData
- Fusion 360