SBIR Phase II: Empowering Music Learning Through Composition on Mobile Devices

This project will address the need for accessible, creative music education. Over 90% of Americans believe music education is valuable, but very few people ever learn enough to create their own music. Instrument lessons are a great way for some beginners to learn about music; however, instrument lessons are expensive and difficult, and focus on performance and technique at the expense of creativity. This project uses a simple audiovisual composition interface to empower music learners to create their own original music on mobile devices from the very beginning of their music education. By combining this intuitive composition interface with data tracking and analysis, this project creates the opportunity to provide music makers with personalized, adaptive feedback as they compose. Currently, $3 billion are spent each year in the United States on instrument lessons, even though they are unaffordable for many potential customers. By leveraging the proliferation of mobile devices worldwide, this project will deliver an accessible, low-cost digital music education option, creating a new market that includes customers who are currently priced out. Expanding participation in creative music education will increase the quantity and quality of music composed worldwide, while also building a sustainable, revenue-generating business and creating new jobs. Through data-driven agile software development, this project will address the need for accessible music education through the creation of a technology platform that delivers adaptive learning to musical beginners. Because the platform upon which this project is built is already empowering the creation of thousands of songs each week and collecting usage data from live users, this project is uniquely positioned to tackle the complex problem of providing algorithmic feedback on creative work at scale. Research and development will proceed in four stages: (1) expanding internal tools to allow for direct analysis of the thousands of songs being created on the platform each week; (2) developing an algorithmic approach to analyzing songs and reporting the results to users; (3) applying analysis to match users with relevant communities and collaborators; and (4) implementing adaptive learning approaches to help users more effectively learn to create music. This staged development process will result in an innovative and highly differentiated technology that enables beginners with no musical experience to compose their own music, and uses data to actively support their individual needs as they learn.