Back in the late 80s when I learned to fly airplanes, my instructor required all the student pilots to fly without the benefit of noise attenuating headsets. In the air, one of the first lessons I learned was that the sound or spectrum of the engine tells a story. If one listens carefully, it can tell us if the mixture needs to be adjusted or if a ring seal is worn loose. Indeed, you can hear many features of an engine, a fact that is crudely implemented with automatic sensors in modern automobile engines. Of course, this type of deduction is not unique to engines, and deducing the features of systems from their spectrum gives rise to a wealth of wonderful mathematical questions known as inverse problems. In the current Education installment, authors Steven Cox, Mark Embree, and Jeffrey Hokanson explore a fascinating inverse problem arising from a vibrating string with beads attached to it, and it is their laboratory apparatus, a monochord, which is featured on the cover of this issue. In the broad field of inverse problems, there are a host of “Can you...?” questions. Perhaps the most familiar of these is “Can you hear the shape of a drum?” The answer was proven to be “no” via counterexample, and the quest to resolve the problem and many of its offshoots led to a large body of excellent mathematics. In this issue's feature, the authors tipped their hand by choosing the title “One Can Hear the Composition of a String: Experiments with an Inverse Eigenvalue Problem.” Specifically, the authors chose an intriguing inverse problem consisting of beads threaded through a massless string of known length under high tension. If we pluck this system, can we use its spectrum to determine masses and positions of the beads? This issue's module provides the instructor or the avid student with all four pillars of applied mathematics: modeling, analysis, computation, and experimentation, though the main thrust is on the latter three. The authors discuss the model and then carefully describe their monochord apparatus for exploring this system experimentally. After collecting displacement data from a single point on the monochord, they use a short MATLAB script to verify that indeed the model captures the essential behavior of the system. The hard part is going the other way: Can we map the eigenvalues of the time series back to bead positions and masses? The authors have already told us that we can, and I will not spoil your fun by revealing too many details, except to say that the authors exploit connections between orthogonal polynomials and the characteristic polynomials of symmetric tridiagonal matrices. The resulting algorithm is robust and, as the results will attest, effective. The article is perfect for advanced undergraduates or graduate students, and it might even inspire some to build a monochord ... or at least turn down the car stereo and listen to the engine from time to time.