Relative Transfer Function Inverse Regression from Low Dimensional Manifold

10/25/2017
by   Ziteng Wang, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/14/2021

Deep Nonparametric Regression on Approximately Low-dimensional Manifolds

In this paper, we study the properties of nonparametric least squares re...
research
06/23/2020

Normalizing Flows Across Dimensions

Real-world data with underlying structure, such as pictures of faces, ar...
research
03/22/2013

Sparse Projections of Medical Images onto Manifolds

Manifold learning has been successfully applied to a variety of medical ...
research
07/08/2020

Low-dimensional Manifold Constrained Disentanglement Network for Metal Artifact Reduction

Deep neural network based methods have achieved promising results for CT...
research
01/22/2016

Exploiting Low-dimensional Structures to Enhance DNN Based Acoustic Modeling in Speech Recognition

We propose to model the acoustic space of deep neural network (DNN) clas...
research
09/15/2011

Reconstruction of sequential data with density models

We introduce the problem of reconstructing a sequence of multidimensiona...
research
10/01/2020

Ray-based classification framework for high-dimensional data

While classification of arbitrary structures in high dimensions may requ...

Please sign up or login with your details

Forgot password? Click here to reset