I-Corps: Music in the Numbers

Suny College At Geneseo, Geneseo NY

Investigators

Abstract

The broader impact/commercial potential of this I-Corps project is driven by novel software within an automated digital engine, performing sonification that translates numerical sequences of archived and real-time data streams into sound. The software has applications in multiple industries that will benefit from ongoing interpretation of big data using non-speech audio to represent data. The audio feed will be paralleled with visual representation, allowing the user instant access to expanded information. The human ear is superior to the eye for processing certain kinds of information, especially information that changes over time. As an alternative or complement to visual analysis, audio perception can process data in temporal, spatial, amplitude, and frequency resolution. Shifting ongoing trend analysis and anomaly detection to audio will reduce visual data overload. Audio interpretation allows users to multi-task while listening to an audio stream of archived or real-time data. Sonification offers audio icons to visually impaired users and provides an alternative learning mode for diverse learners. It is an efficacious tool for trend analysis and anomaly detection for diverse industries, such as health, medicine, finance, defense, aviation, manufacturing, security, including homeland and home security fields, and other endeavors requiring analysis of big data. This I-Corps project was born from an Antarctic Artists and Writers Program award from NSF's Office of Polar Programs. It represents the fusion of artistic and scientific expertise toward the development of a new data analysis tool. This project combines both automated sonification of data with customized interpretation that responds to a variety of aesthetic and ethnic sensibilities. Audio backgrounds can be customized based upon diverse music styles, including but not limited to, jazz, calypso, classical, rock. Elements of customization happen outside of the algorithm. The algorithm produces a melody of notes coming from a data feed. User customizations provide an appropriate customized rhythm track as well as a chord progression or program that would harmonize with the melody produced. Information overload lowers employee productivity, according to Harvard Business Review (Hemp 2009). Given this overload and data analysis needs in many markets, this product could be launched in diverse markets, including financial trading, intensive care medicine, factory process control, air traffic control, security monitoring, soldier awareness, and markets with unmanageable flows of critical real time data with resulting data comprehension and response time errors. 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.