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Deciphering the Secrets of Musical Intelligence: Big Data Analysis Techniques and End-User Tools for Music Score

Evaluating the feasibility of using sound to track honey bee recruitment dances

As part of their trade, musicians continuously study and analyze music scores. Important musical tasks such as selecting repertoire require searching scores for their constituent elements. Searching collections of scores can be burdensome and cognitively taxing even for expert musicologists. Despite the promise of computing to facilitate searching over vast volumes of information, commercial search engines only operate on score metadata (e.g., composer, time period, genre, etc.). In this project, we have explored the user requirements, design choices, and software architecture of a search engine for querying music scores beyond metadata (e.g., instrument range, key/time signature, dynamics, etc.). We have also developed our proof-of-concept implementation of this architecture Ask Toscanini! which supports a wide selection of user queries, expressed as structured text strings, against a collection of digital scores. The engine can
search through any collection of scores, provided in the popular standardized MusicXML format, which is subsequently transformed into our custom search-efficient format. In addition to ensuring search efficiency, our design also renders the engine amenable for use by musicians, the majority of whom are not computing experts. The insights derived from this project can help future research efforts in enhancing search technologies for music scores and can serve as a
blueprint for creating commercial solutions in this domain.

Project Outcomes:
Our proof-of-concept search engine for music scores can be found at: http://toscanini.cs.vt.edu/

Submissions for publication:
We submitted a paper to the 5th International Conference on Digital Libraries for Musicology (DLFM 2018) (https://dlfm.web.ox.ac.uk/). The draft can be accessed at: https://drive.google.com/open?id=1l_z4Gb_36vkIpCPmi86quW3UbYIRPLmP

Demonstrated the research project to incoming freshmen for the CEED Women's Preview Weekend on April 8th, 2018

Virginia Tech Undergraduate Research in Computer Science (VTURCS) symposium poster, Spring 2018: https://drive.google.com/open?id=1nhGRP_D9neh4JqLodUdtYQR6THs6a2qMWX2_4uxekL0

Student Involvement:
Graduate Students: Arman Bahraini and Spencer Lee Undergraduate Students: Galina Belolipetski, Taber Fisher, Matthew Fishman, and Michael Mills
High School Student: Benjamin June, (Blacksburg High School)

Educational components (K-12):
Mentored a Blacksburg High School junior student to work on the requirements component of the project. The student’s project involved surveying band directors about their needs for searching scores when selecting repertoire for their performance ensembles. The student received an Honorable Mention Award at the Blue Ridge Highlands Regional Science Fair.

Supplemental resources used to complete the project:

Media coverage:
The project team won Second Place Faculty Choice Award at the 2018 Virginia Tech Undergraduate Research in Computer Science (VTURCS) symposium (https://www.vturcs.cs.vt.edu/spring18.php)

Anticipated external funding which may result from this project:
A proposal to the National Endowment for Humanities (NEH) digital humanities advancement program is currently under review (https://www.neh.gov/grants/odh/digital-humanities-advancement-grants)

Dr. Eli Tilevich, Department of Computer Science
Dr. Charles Nichols, School of Performing Arts

Social Sciences
Performing Arts
Computer Science