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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.

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Professor:

Prof. Dr.-Ing. Walter Kellermann

Supervisior:

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

Available:

Immediately