48 citations · 86 across the 15 of their papers we have counts for
15 papers
DNN-Based Frequency-Dependent Estimation of Speech, Music, and Noise Power in Acoustic Mixtures for Hearing-Aid Scene Analysis
Mats Lang, Thomas Haubner, Nina Kiessling +2
Acoustic scene analysis is essential for adapting hearing-aid signal processing algorithms to the current listening environment. However, state-of-the-art (SOTA) systems typically…
A Novel Binaural Cue Preservation Loss for DNN-Based Binaural Speech Enhancement
Jayteerth Amble, Thomas Haubner, Hendrik Schröter +2
Binaural speech enhancement for hearing aids aims to reduce noise while preserving the interaural cues needed for spatial localization. Although deep neural network-based methods a…
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…
Localizing Spatial Information in Neural Spatiospectral Filters
Annika Briegleb, Thomas Haubner, Vasileios Belagiannis +1
Beamforming for multichannel speech enhancement relies on the estimation of spatial characteristics of the acoustic scene. In its simplest form, the delay-and-sum beamformer (DSB)…
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…
Joint Acoustic Echo Cancellation and Blind Source Extraction based on Independent Vector Extraction
Thomas Haubner, Zbyněk Koldovský, Walter Kellermann
We describe a joint acoustic echo cancellation (AEC) and blind source extraction (BSE) approach for multi-microphone acoustic frontends. The proposed algorithm blindly estimates AE…