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Auditory Scene Analysis
Awards
Kay H. Brodersen received a Trainee Abstract Award at HBM 2012 for his work on 'Model-Based Clustering Using Generative Embedding.' Kay will be giving a talk on his results in Beijing on 12 June.
François Cellier received the McLeod Founder's Award of the Society for Modeling and Simulation International.
Spotlight
Computational models of brain connectivity, coupled with machine learning algorithms, make it possible to infer neuronal disease mechanisms from
non-invasive functional magnetic resonance imaging (fMRI) data in humans.
This illustration shows how dynamic systems models can be used for reducing
complex (high-dimensional) brain activity data to a simple (low-dimensional)
and mechanistically interpretable representation (Brodersen et al., PLoS
Comput. Biol. 2011). Please also see the summary on ETH Life.
Introduction
| The human auditory system selects relevant sounds from noise and irrelevant acoustic input. For hearing impaired persons, this ability is often significantly reduced. Furthermore, resolution in time and frequency is degraded, which makes it difficult to accurately locate a source. In collaboration with Phonak AG, we develop methods to analyze acoustic scenes and hearing instrument wearers' needs, with the goal of optimal adaptive control of the hearing instrument. Our current research focuses on hierarchical classification, component analysis, unsupervised and semi-supervised online learning, and model based signal processing. |
Projects
Component analysis to enhance speech in noisy environments
| The speech we hear and the facial movements we see have a high degree of statistical dependence. Therefore, a person can better follow a conversation if she sees her discussion partner's face. We design algorithms which exploit this dependency to enhance speech in difficult listening environments. | ||
 Two observations of the same source: A sequence of video frames of a speaker's face, and the distribution of spectral energy of the speech over time. |
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| Analysis procedures such as PCA reveal effects which are present in a data set. They also provide explanations of these effects in terms of linear combinations of influence factors. In order to interpret them, sparse explanations which depend only on few influence factors are preferred. We develop algorithms which can handle large data sets with millions of samples and influence factors efficiently. | ||
 A three-dimensional data set showing a superposition of two effects. One is well explained by the influence factors plotted horizontally, the other by the single influence factor plotted vertically. |
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Contact: Tomas Dikk, Christian Sigg |
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Collaborators:* Phonak AG, Dr. Launer |
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Funding source: Phonak AG, KTI |
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