Award Abstract # 2222369
Collaborative Research: FW-HTF-R: Toward an Ecosystem of Artificial-intelligence-powered Music Production (TEAMuP)

NSF Org: IIS
Division of Information & Intelligent Systems
Recipient: NORTHWESTERN UNIVERSITY
Initial Amendment Date: September 12, 2022
Latest Amendment Date: September 12, 2022
Award Number: 2222369
Award Instrument: Standard Grant
Program Manager: Scott Robertson
sroberts@nsf.gov
 (703)292-2971
IIS
 Division of Information & Intelligent Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: October 1, 2022
End Date: September 30, 2026 (Estimated)
Total Intended Award Amount: $386,139.00
Total Awarded Amount to Date: $386,139.00
Funds Obligated to Date: FY 2022 = $386,139.00
History of Investigator:
  • Bryan Pardo (Principal Investigator)
    pardo@northwestern.edu
Recipient Sponsored Research Office: Northwestern University
633 CLARK ST
EVANSTON
IL  US  60208-0001
(312)503-7955
Sponsor Congressional District: 09
Primary Place of Performance: Northwestern University
2233 Tech Drive
Evanston
IL  US  60208-3106
Primary Place of Performance
Congressional District:
09
Unique Entity Identifier (UEI): EXZVPWZBLUE8
Parent UEI:
NSF Program(s): FW-HTF Futr Wrk Hum-Tech Frntr
Primary Program Source: 01002223DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 063Z
Program Element Code(s): 103Y00
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041, 47.070

ABSTRACT

This project builds the foundations of a new ecosystem for music production to empower future musicians to better leverage Artificial Intelligence (AI) tools in the creation, performance, and dissemination of their music, while also accelerating audio AI research. This involves the creation of both an open-access software framework enabling musicians and researchers to collaborate in the development and use of ever-better AI-powered tools for music creation, and a set of initiatives to enable a critical mass of musicians to use these tools in transformative ways. Musicians are expected to use these tools to produce lower-cost, higher-quality music products, which meet growing demand for digital music content for videos, websites, advertising, audio recordings, and other new media. Enabling musicians to be more self-sufficient in their music creation has the potential to increase the number of musically talented individuals that will be able to make a living with their art, especially from currently under-represented populations. To enable growing musicians to make full use of AI tools, a set of innovative learning experiences to acquire the needed mindsets and skills will be developed and field tested in a 2-semester course for students with music interests and a ?Summer Camp? for pre-college under-represented youth, along with the creation of online instructional materials to support specific learning experiences in a variety of settings.

The project team possesses complementary disciplinary expertise in music, audio-engineering, AI, learning sciences/ education, business/ entrepreneurship, ethics, and inclusion. These skills will be brought to bear on developing a framework for a commonly-used free and open-source digital audio platform that will allow: (a) audio AI researchers to easily deploy their new AI models into the platform; and, b) musicians who use these AI tools to share their music productions with AI researchers so they can refine their models. Interviews and surveys will also be conducted with diverse musicians to better understand key factors that may affect their adoption of AI music production tools and how those tools may transform their work, as well as the implications of the pandemic and other barriers that may be experienced by under-represented populations in music production. Together, the project will generate a better understanding of factors that may affect musicians? adoption and transformative use of AI in their work, understanding which could be generalized to other occupations at the human-technology frontier. Finally, the team will develop pedagogical principles and practices that can inform the design of effective educational interventions to better prepare future musicians and other domain experts to leverage technology.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Flores_Garcia, H and Seetharaman, P and Kumar, R and Pardo, B "VampNet: Music Generation via Masked Acoustic Token Modeling" , 2023 Citation Details

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