# Source Localization using Manifold Learning

## Proposal for a Master Thesis

### Topic:

Source Localization using Manifold Learning

### Description:

Acoustic source localization is a precondition for many subsequent signal processing

algorithms. It can be accomplished by mapping one or more acoustic features via an

analytical mathematical model or a data-driven model, learned by a machine learning

approach, to the position of the source. Most acoustic localization algorithms use

directional features, e.g., phase differences, time difference of arrivals etc., due to the

simplicity of the underlying geometric model. An alternative approach is obtained by

the following observation: The spatial information of an acoustic source is embedded

in the corresponding room impulse response (RIR) as sources at different positions

evoke different reflection patterns in an enclosure. Thus, a feature vector depending

on the RIRs corresponding to two observing microphones can be obtained by the

relative transfer function (RTF).

However, the RTFs are controlled by only a few parameters, e.g., the position of

the source, the reflection patterns of the walls, room volume etc., which gives rise

to the assumption that there is an invertible mapping from the space of the highdimensional RTFs to the low-dimensional parameter space, i.e., the RTFs lie on a

manifold. By applying so-called manifold learning techniques, the structure of the

RTFs and the mapping to the low-dimensional parameter space can be learned, i.e.,

observed RTFs with unknown corresponding position can be used to localize the

acoustic source by evaluating the learned mapping.

The aim of this thesis is the implementation and evaluation of algorithms for

acoustic source localization based on manifold learning, starting with direction of

arrival estimation and position estimation based on a single microphone array and

continuing with their extension to acoustic sensor networks.

As prerequisites, the student should have MATLAB programming experience and

an affinity to math.

### Professor:

Prof. Dr.-Ing. Walter Kellermann

### Supervisior:

M.Sc. Andreas Brendel, room 05.018, Andreas.Brendel@FAU.de

### Available:

Immediately