Music expresses and elicits emotion. But how, exactly?

Philosophers have been fascinated by the question since Pythagoras. At the USC Viterbi School of Engineering, Elaine Chew, assistant professor of industrial and systems engineering, teaches a graduate course on using computational and other engineering tools to look for answers.

Chew, who continues a career as a distinguished concert pianist in addition to her engineering research, has written an account of the issues in creating the course for an article to be presented at an engineering conference later this year.

Meanwhile, the students in “Computational Modeling of Expressive Performance” presented their results, a collection of 10 projects now up on the class Web site, as what Chew calls a “non-peer reviewed publication” at www-scf.usc.edu/~ise575/b/projectshttp://www-scf.usc.edu/~ise575/b/projects.

The projects from the class, offered for only the fourth time during the spring semested, had a wide range. Arpi Mardirossian, who found a trove of silent movie scores specially written to evoke specific emotions, analyzed their characteristics.

For “Analysis of Dynamic Shaping in Unaccompanied Bach,” student Eric Cheng created intricate graphs comparing, note by note, performances of an unaccompanied Bach violin sonata by three master violinists: Jascha Heifetz, Yehudi Menuhin and Nathan Milstein, with respect to their dynamic shaping, i.e., the nature and amount of sound intensity variation.

Meghen Miles and Merrick Mosst created “Emotiongrams” by mapping specific musical characteristics (e.g., minor keys), widely identified with certain emotions (e.g., sadness), to color patterns that represented varying combinations of energy.

The other seven topics posed equally interdisciplinary questions – questions that are quite new in engineering in general, and engineering education in particular.

In a conference publication that will be presented this fall in San Diego, “A Case Study in Course Design at the Intersection of Music and Engineering,” Chew details the challenges in creating the class and discusses the problems that remain.

Chew traces the beginning of modern efforts to bring engineering techniques into a musical analysis of Christopher Longuet-Huggins, a noted theoretical chemist, cognitive scientist and gifted amateur musician, who in his “Letters to a Musical Friend” (The Musical Review, 1962) described computing methods for “Interpreting Bach” that were implemented and published in Machine Intelligence in 1971.

While interest in the field has mushroomed in the 21st century, with engineering conferences and refereed journals now covering the subject, teaching is only beginning.

“The challenges include the lack of a formal body of knowledge, in the form of a text, the lack of formal academic structures to support the course, the lack of students with suitably strong backgrounds in both computing and music, and misconceptions about the nature of music research,” she notes in her presentation.

Attacking problems in computational modeling of music “draws upon methodologies and tools from music theory, cognitive science, artificial intelligence, experimental psychology, mathematics, signal processing and neuroscience. Few, if any, students enrolled in the course are equipped with the knowledge to understand all the material.”

The syllabus devised offers a crash course in the elements of all these disciplines touching on music cognition, with the aim that, at the end of the course, each student should be able to “understand basic music structures; be capable of manipulating digital music; be able to generate computational means of analyzing, generating and visualizing structured music; and be able to formulate a question and build the computational tools to answer it.”

The course’s final projects depend on the students having acquired at least the rudiments of all these skills. The amount of material that has to be mastered is so great that Chew reluctantly had to move what had been a popular feature of the course – guest appearances by musicians and researchers in the field – to its own separate series.

Precisely because of the unusual mixture of disciplines involved, making this course a reality was not institutionally easy, Chew said. However, a remarkable record of recognition for her research (she holds a Ph.D. from MIT in engineering and won an Early Career/PECASE award from the National Science Foundation) helped to pave the way.

“All these interdisciplinary education and outreach activities could not have happened without the staunch support of my own department, the deans of the Viterbi School, and the provost,” Chew said. “My own department has graciously allowed me to create this special topics class in lieu of teaching another traditional industrial and systems engineering course. My department chair, James Moore, has encouraged me to forge ahead in creating an undergraduate counterpart to the class because he sees the potential of such courses in recruitment and retention of young and inquiring minds to engineering.”

With this kind of class, it’s not just a matter of “if you build it, they will come,” Chew said. Recruiting students was a major effort. “I personally and actively publicized the class widely through e-mails and posters to other departments in the Viterbi and Thornton schools and the College.”

So, how does music express and evoke emotion? Chew and her students don’t know yet. But check back in a few years.

Chew’s research was supported by the National Science Foundation. Her presentation will be made at the 36th ASEE/IEEE Frontiers in Education Conference, Oct. 28-31, in San Diego.