Interpreting the input admittance of violins and guitars

The most widespread vibration measurement on musical instrument bodies is of the point mobility at the bridge. Analysis of such measurements is presented, with a view to assessing what range of information could feasibly be extracted from the corpus of data. Analysis approaches include (1) pole-residue extraction; (2) damping trend analysis based on time decay information; (3) statistical estimates based on SEA power-balance and variance theory. Comparative results are shown for some key quantities. Damping trends with frequency are shown to have unexpectedly different forms for violins and for guitars. Linear averaging to estimate the "direct field" component gives a simple and clear visualisation of any local resonance behaviour near the bridge, such as the "bridge hill", and reveals some violins that show a double hill, while viols show only weak hills, and guitars none at all. © S. Hirzel Verlag · EAA.

Rank-preserving geometric means of positive semi-definite matrices

The generalization of the geometric mean of positive scalars to positive definite matrices has attracted considerable attention since the seminal work of Ando. The paper generalizes this framework of matrix means by proposing the definition of a rank-preserving mean for two or an arbitrary number of positive semi-definite matrices of fixed rank. The proposed mean is shown to be geometric in that it satisfies all the expected properties of a rank-preserving geometric mean. The work is motivated by operations on low-rank approximations of positive definite matrices in high-dimensional spaces.© 2012 Elsevier Inc. All rights reserved.

Regression on fixed-rank positive semidefinite matrices: A Riemannian approach

The paper addresses the problem of learning a regression model parameterized by a fixed-rank positive semidefinite matrix. The focus is on the nonlinear nature of the search space and on scalability to high-dimensional problems. The mathematical developments rely on the theory of gradient descent algorithms adapted to the Riemannian geometry that underlies the set of fixedrank positive semidefinite matrices. In contrast with previous contributions in the literature, no restrictions are imposed on the range space of the learned matrix. The resulting algorithms maintain a linear complexity in the problem size and enjoy important invariance properties. We apply the proposed algorithms to the problem of learning a distance function parameterized by a positive semidefinite matrix. Good performance is observed on classical benchmarks. © 2011 Gilles Meyer, Silvere Bonnabel and Rodolphe Sepulchre.