However diverse around the world, a majority of musical practice involves tools and instruments. As such, breakthroughs and paradigm shifts in technology–the craft of creating and developing these tools–can greatly influence innovation in music. An example of this is the commodification of electronic equipment for musical purposes in the form of recording studios in the 1950s, allowing for artistic experimentation and enabling new ways to produce music.
Usage of novel technology in music practice is often a complex process that initially requires specialists to take care of the technical aspects. In the case of early recording studio technology, an example of this is the collaboration between engineer Pierre Schaeffer and musician Pierre Henry, who in the ’50s collaborated closely to find a new musical usage of the technology, thus inventing the sampler, among other innovations.
More recently, advances in artificial intelligence (AI) and machine learning promise to be transformative for music practice. The prospect of machines mimicking complex human behavior and performing tasks that humans are not capable of triggers the imagination of different ways to produce music. AI-based technology as a driver for innovation in musical practice appears to be at the same stage as the recording studio was in the 1950s and 1960s: the technology exists, but making it available as a tool for music production requires specialists (AI engineers) operating the technology and assisting musicians in its usage.
As a musical research lab developing AI-based tools for innovation in music practice, we believe that musicians and engineers working in unison is vital to the innovation process. It helps to overcome the dual problems that, on the one hand, engineers may not be aware of what musicians look for artistically, and on the other hand that musicians may not be aware of the opportunities that the technology offers for novel music practices.