1 research outputs found
Relative Transfer Function Inverse Regression from Low Dimensional Manifold
In room acoustic environments, the Relative Transfer Functions (RTFs) are
controlled by few underlying modes of variability. Accordingly, they are
confined to a low-dimensional manifold. In this letter, we investigate a RTF
inverse regression problem, the task of which is to generate the
high-dimensional responses from their low-dimensional representations. The
problem is addressed from a pure data-driven perspective and a supervised Deep
Neural Network (DNN) model is applied to learn a mapping from the
source-receiver poses (positions and orientations) to the frequency domain RTF
vectors. The experiments show promising results: the model achieves lower
prediction error of the RTF than the free field assumption. However, it fails
to compete with the linear interpolation technique in small sampling distances.Comment: 5 pages, in preparation for Signal Processing Letter