Briegleb, Annika
Annika Briegleb, M. Sc.
Research
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.
Theses (completed/in progress)
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)
Publications
2023
Exploiting spatial information with the informed complex-valued spatial autoencoder for target speaker extraction
2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (Rhodos, 4. June 2023 - 10. June 2023)
DOI: 10.1109/ICASSP49357.2023.10095196
URL: https://ieeexplore.ieee.org/document/10095196
BibTeX: Download
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2022
Statistical Analysis of Randomness in Training of Small-Scale Neural Networks for Speech Enhancement
2022 International Workshop on Acoustic Signal Enhancement (IWAENC) (Bamberg, 5. September 2022 - 8. September 2022)
DOI: 10.1109/IWAENC53105.2022.9914739
URL: https://ieeexplore.ieee.org/document/9914739
BibTeX: Download
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2021
Combining Adaptive Filtering and Complex-valued Deep Postfiltering for Acoustic Echo Cancellation
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (Toronto, 6. June 2021 - 11. June 2021)
In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2021
DOI: 10.1109/ICASSP39728.2021.9414868
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2019
Deep Clustering for single-channel ego-noise suppression
International Congress on Acoustics (ICA) (Aachen, 9. September 2019 - 13. September 2019)
URL: https://pub.dega-akustik.de/ICA2019/data/articles/000705.pdf
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