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We propose a dynamic optimization approach to calculate optimal feedforward controls for exact path-following problems of differentially flat systems. Besides the derivation of a small dimensional optimal control problem, we provide easily checkable condit ...
The following article presents a novel, adaptive initialization scheme that can be applied to most state-ofthe-art Speaker Diarization algorithms, i.e. algorithms that use agglomerative hierarchical clustering with Bayesian Information Criterion (BIC) and ...
We investigate the problem of distributed sensors' failure detection in networks with a small number of defective sensors, whose measurements differ significantly from neighbouring sensor measurements. We build on the sparse nature of the binary sensor fai ...
This paper investigates a neural network based acoustic feature mapping to extract robust features for automatic speech recognition (ASR) of overlapping speech. In our preliminary studies, we trained neural networks to learn the mapping from log mel filter ...
We address the problem of person independent 3D gaze estimation using a remote, low resolution, RGB-D camera. The approach relies on a sparse technique to reconstruct normalized eye test images from a gaze appearance model (a set of eye image/gaze pairs) a ...
The early detection of changes in the level and composition of algae is essential for tracking water quality and environmental changes. Current approaches require the collection of a specimen which is later analyzed in a laboratory: this slow and expensive ...
The following article presents a novel, adaptive initialization scheme that can be applied to most state-ofthe-art Speaker Diarization algorithms, i.e. algorithms that use agglomerative hierarchical clustering with Bayesian Information Criterion (BIC) and ...
Artificial neural networks represent a simple but efficient way to model and correct known errors existing between commonly used density functional computations and experimental data. The recently proposed X1 approach combines B3LYP energies with a neural- ...
In this paper, we present a novel semi-supervised dimensionality reduction technique to address the problems of inefficient learning and costly computation in coping with high-dimensional data. Our method named the dual subspace projections (DSP) embeds hi ...
This paper presents our approach for automatic speech recognition (ASR) of overlapping speech. Our system consists of two principal components: a speech separation component and a feature estmation component. In the speech separation phase, we first estima ...