Briegleb, Annika

Annika Briegleb, M. Sc.

Department of Electrical-Electronic-Communication Engineering
Chair of Multimedia Communications and Signal Processing (Prof. Dr. Kaup)

Room: Room 05.019
Cauerstr. 7
91058 Erlangen


My research focuses on audio signal enhancement in various situations, including robot audition. I predominantly work on machine learning-based methods and also investigate combinations of model- and data-driven approaches.


  • Lab Course Statistical Signal Processing
    Further information can be found on StudOn and Campo.

Theses (completed/in progress)

Master theses:

  • 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 (20

Bachelor theses:

  • 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)

Research projects/internships:

  • 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)