Christian Hümmer



Organisation


Professur für Nachrichtentechnik
Lehrstuhl für Hochfrequenztechnik


Publications (Download BibTeX)

Go to first page Go to previous page 2 of 3 Go to next page Go to last page

Hümmer, C., Stadter, P., & Kellermann, W. (2016). Uncertainty decoding using a sampling strategy based on the eigenvalue decomposition. In VDE (Eds.), Proceedings of the 12th ITG Symposium on Speech Communication (pp. 362-366). Paderborn, DE.
Hümmer, C., Maas, R., Hofmann, C., & Kellermann, W. (2015). A Bayesian network approach to linear and nonlinear acoustic echo cancellation. Eurasip Journal on Advances in Signal Processing, 2015(1), 1-11. https://dx.doi.org/10.1186/s13634-015-0282-2
Maas, R., Hümmer, C., Sehr, A., & Kellermann, W. (2015). A Bayesian view on acoustic model-based techniques for robust speech recognition. Eurasip Journal on Advances in Signal Processing, 2015(1), 1-16. https://dx.doi.org/10.1186/s13634-015-0287-x
Barfuß, H., Hümmer, C., Lamani, G., Schwarz, A., & Kellermann, W. (2015). HRTF-based robust least-squares frequency-invariant beamforming. In Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) (pp. 1 - 5). New Paltz, NY, US.
Schwarz, A., Hümmer, C., Maas, R., & Kellermann, W. (2015). Spatial diffuseness features for DNN-based speech recognition in noisy and reverberant environments. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (pp. 4380-4384). Brisbane, AU: Institute of Electrical and Electronics Engineers Inc..
Hümmer, C., Maas, R., & Kellermann, W. (2015). The NLMS algorithm with time-variant optimum stepsize derived from a Bayesian network perspective. IEEE Signal Processing Letters, 22(11), 1874-1878. https://dx.doi.org/10.1109/LSP.2015.2439392
Hümmer, C., Maas, R., Schwarz, A., Astudillo, R.F., & Kellermann, W. (2015). Uncertainty decoding for DNN-HMM hybrid systems based on numerical sampling. In Proceedings of the Annual Conference of the International Speech Communication Association (Interspeech) (pp. 3556-3560). Dresden, DE.
Maas, R., Hümmer, C., Schwarz, A., Hofmann, C., & Kellermann, W. (2014). A Bayesian network view on linear and nonlinear acoustic echo cancellation. In Proceedings of the 2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 (pp. 495-499). Xi'an, CN.
Maas, R., Hümmer, C., Hofmann, C., & Kellermann, W. (2014). On Bayesian networks in speech signal processing. In Proceedings of the 11. ITG Symposium on Speech Communication (pp. 1-4). Erlangen, DE: VDE.
Hofmann, C., Hümmer, C., & Kellermann, W. (2014). Significance-aware Hammerstein group models for nonlinear acoustic echo cancellation. In Proceedings of the 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (pp. 5934-5938). Florence, IT: Institute of Electrical and Electronics Engineers Inc..

Last updated on 2019-15-03 at 22:54