Briegleb, Annika

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.

Master theses:

• 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:

• Investigation of the STFT in the context of neural network-based speech enhancement (2022)
• Attention Models for Speech Processing (2020)

Research projects/internships:

• 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)