Learning Data-Dependent Transformations for Ego-Noise Suppression

Proposal for a Master Thesis


Learning Data-Dependent Transformations for Ego-Noise Suppression


Robot audition describes the research area of human-robot interaction by speech.
Therefore, robots are often equipped with microphone arrays to capture their surrounding acoustic scene. If the robot is moving, the recorded microphone signals
are significantly distorted by self-induced noise emitted from the various moving
mechanical parts of the robot.
Various algorithms have been proposed to deal with this problem, e.g., [1]. Most
approaches work in a transform domain, i.e., classically the STFT domain due to its
sparsifying nature for speech signals. Recently, increasing research effort has been
spent on learning transformations based on training data. An often used objective is
to enforce sparsity in the transform domain, e.g., [2]. Learning transformations instead of employing data-independent ones has the merit of tailoring transformations
to specific applications.
In this thesis the potential of learning transformations for ego-noise suppression
should be examined. The implemented algorithms should be evaluated against
well-known STFT-based approaches with respect to their effect on noise suppression
As prerequisites, the student should have interest in signal processing and machine
learning algorithms, affinity to math and Matlab programming experience.



Prof. Dr.-Ing. Walter Kellermann


M.Sc. Thomas Haubner, room 05.018 (Cauerstr. 7),