48 citations · 97 across the 15 of their papers we have counts for
16 papers
End-To-End Deep Learning-based Adaptation Control for Linear Acoustic Echo Cancellation
Thomas Haubner, Andreas Brendel, Walter Kellermann
The attenuation of acoustic loudspeaker echoes remains to be one of the open challenges to achieve pleasant full-duplex hands free speech communication. In many modern signal enhan…
A Unifying View on Blind Source Separation of Convolutive Mixtures based on Independent Component Analysis
Andreas Brendel, Thomas Haubner, Walter Kellermann
In many daily-life scenarios, acoustic sources recorded in an enclosure can only be observed with other interfering sources. Hence, convolutive Blind Source Separation (BSS) is a c…
Microphone Utility Estimation in Acoustic Sensor Networks using Single-Channel Signal Features
Michael Günther, Andreas Brendel, Walter Kellermann
In multichannel signal processing with distributed sensors, choosing the optimal subset of observed sensor signals to be exploited is crucial in order to maximize algorithmic perfo…
Manifold learning-supported estimation of relative transfer functions for spatial filtering
Andreas Brendel, Johannes Zeitler, Walter Kellermann
Many spatial filtering algorithms used for voice capture in, e.g., teleconferencing applications, can benefit from or even rely on knowledge of Relative Transfer Functions (RTFs).…
End-To-End Deep Learning-Based Adaptation Control for Frequency-Domain Adaptive System Identification
Thomas Haubner, Andreas Brendel, Walter Kellermann
We present a novel end-to-end deep learning-based adaptation control algorithm for frequency-domain adaptive system identification. The proposed method exploits a deep neural netwo…
Online Acoustic System Identification Exploiting Kalman Filtering and an Adaptive Impulse Response Subspace Model
Thomas Haubner, Andreas Brendel, Walter Kellermann
We introduce a novel algorithm for online estimation of acoustic impulse responses (AIRs) which allows for fast convergence by exploiting prior knowledge about the fundamental stru…
A Synergistic Kalman- and Deep Postfiltering Approach to Acoustic Echo Cancellation
Thomas Haubner, Mhd. Modar Halimeh, Andreas Brendel +1
We introduce a synergistic approach to double-talk robust acoustic echo cancellation combining adaptive Kalman filtering with a deep neural network-based postfilter. The proposed a…
Misalignment Recognition in Acoustic Sensor Networks using a Semi-supervised Source Estimation Method and Markov Random Fields
Gabriel F Miller, Andreas Brendel, Walter Kellermann +1
In this paper, we consider the problem of acoustic source localization by acoustic sensor networks (ASNs) using a promising, learning-based technique that adapts to the acoustic en…
Accelerating Auxiliary Function-based Independent Vector Analysis
Andreas Brendel, Walter Kellermann
Independent Vector Analysis (IVA) is an effective approach for Blind Source Separation (BSS) of convolutive mixtures of audio signals. As a practical realization of an IVA-based BS…
Online Supervised Acoustic System Identification exploiting Prelearned Local Affine Subspace Models
Thomas Haubner, Andreas Brendel, Walter Kellermann
In this paper we present a novel algorithm for improved block-online supervised acoustic system identification in adverse noise scenarios by exploiting prior knowledge about the sp…
Noise-Robust Adaptation Control for Supervised Acoustic System Identification Exploiting A Noise Dictionary
Thomas Haubner, Andreas Brendel, Mohamed Elminshawi +1
We present a noise-robust adaptation control strategy for block-online supervised acoustic system identification by exploiting a noise dictionary. The proposed algorithm takes adva…
Deep Neural Network based Distance Estimation for Geometry Calibration in Acoustic Sensor Networks
Tobias Gburrek, Joerg Schmalenstroeer, Andreas Brendel +2
We present an approach to deep neural network based (DNN-based) distance estimation in reverberant rooms for supporting geometry calibration tasks in wireless acoustic sensor netwo…
Faster IVA: Update Rules for Independent Vector Analysis based on Negentropy and the Majorize-Minimize Principle
Andreas Brendel, Walter Kellermann
Algorithms for Blind Source Separation (BSS) of acoustic signals require efficient and fast converging optimization strategies to adapt to nonstationary signal statistics and time-…
A Unified Bayesian View on Spatially Informed Source Separation and Extraction based on Independent Vector Analysis
Andreas Brendel, Thomas Haubner, Walter Kellermann
Signal separation and extraction are important tasks for devices recording audio signals in real environments which, aside from the desired sources, often contain several interferi…
Spatially Informed Independent Vector Analysis
Andreas Brendel, Thomas Haubner, Walter Kellermann
We present a Maximum A Posteriori (MAP) derivation of the Independent Vector Analysis (IVA) algorithm, a blind source separation algorithm, by incorporating a prior over the demixi…
Single-Channel Maximum-Likelihood T60 Estimation Exploiting Subband Information
Heinrich Loellmann, Andreas Brendel, Peter Vary +1
This contribution presents four algorithms developed by the authors for single-channel fullband and subband T60 estimation within the ACE challenge. The blind estimation of the ful…