Speaker: Emmanuel Vincent
Date: June 07, 2018
Abstract:
Prior knowledge of the relative transfer function (RTF) is
useful in many applications but remains little studied. In this work, we
propose a semi-supervised learning algorithm based on deep neural
networks (DNNs) for RTF inverse regression, that is to generate the
full-band RTF vector directly from the source-receiver pose (position
and orientation). Two typical scenarios are discussed: training on
labeled RTFs only, or on additional unlabeled RTFs. Both setups utilize
the low-dimensional manifold property of RTF in stationary environments.
With this property as an additional regularization term, a smooth
mapping solution with respect to the manifold is obtained. Experimental
simulations show that the proposed method achieves a lower mean
prediction error than the free field model with few labeled RTFs, and
the unlabeled RTFs are essential in improving the inverse regression
performance.
