Briegleb, Annika

Briegleb, Annika

 

Mein Forschungsschwerpunkt liegt auf der Audiosignalverbesserung in diversen Szenarien, u.a. dem robotischen Hören. Dabei untersuche ich hauptsächlich Methoden des maschinellen Lernens und Kombinationen aus modell- und datengetriebenen Verfahren.

Laborpraktikum zur Statistischen Signalverarbeitung
Weitere Informationen sind auf StudOn und Campo zu finden.

Masterarbeiten:

  • Complex-valued Variational Autoencoder for Speech Enhancement (2023)
  • Dual-staging in speech enhancement: An analysis of cost function modalities (2022)
  • Complex-valued Variational Autoencoder for Speech Enhancement (2022)
  • Exploring Attention Models for Speech Enhancement (2021)
  • Acoustic Source Separation based on Deep Clustering and Independent Component Analysis (2021)
  • An Evaluation of the Perception-based Loss for Speech Enhancement (2021)
  • A Denoising Autoencoder for Speech Enhancement (2020)
  • Deep Attractor Networks for single-channel ego-noise reduction in robot audition (2020)

Bachelorarbeiten:

  • Analysis of RNN features in spatiospectral filtering (2024)
  • Investigation of the STFT in the context of neural network-based speech enhancement (2022)
  • Attention Models for Speech Processing (2020)

Forschungsprojekte:

  • Deep learning-based spatial filtering for audio processing (2023)
  • Effect of a 3D convolutional layer on multichannel speech enhancement (2023)
  • Influence of training target on spatial filtering behavior in multichannel speech enhancement (2023)
  • Experimental study on performance variability in neural networks due to hardware and software involved in training (2022)
  • Postprocessing for mask-based speech enhancement (2022)
  • Evaluation of cost functions for neural network-based postfiltering in acoustic echo cancellation (2021)
  • An end-to-end ASR system for speech enhancement (2021)
  • Learning a Transformation for Audio Signal Representation (2021)
  • Evaluation of Deep Clustering for discriminating various types of robotic ego-noise (2020)
  • Hyperparameter adaptation for Deep Clustering for ego-noise suppression (2020)

2023

2022

2021

2019