Prof. Dr. Emanuël Habets



Organisation


Professur für wahrnehmungsbasierte räumliche Audiosignalverarbeitung (AudioLabs) (Stiftungsprofessur)


Publications (Download BibTeX)

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Thiergart, O., Milano, G., & Habets, E. (2019). Combining linear spatial filtering and non-linear parametric processing for high-quality spatial sound capturing. In Proc. IEEEIntl. Conf. on Acoustics, Speech and Signal Processing (ICASSP). UK.
Stöter, F.-R., Chakrabarty, S., Edler, B., & Habets, E. (2019). CountNet: Estimating the number of concurrent speakers using supervised learning. IEEE/ACM Transactions on Audio, Speech and Language Processing, 27(2), 268--282. https://dx.doi.org/10.1109/TASLP.2018.2877892
Herzog, A., & Habets, E. (2019). Direction preserving Wiener matrix filtering for Ambisonic input-output systems. In Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP). UK.
Herzog, A., & Habets, E. (2019). Eigenbeam-ESPRIT for DOA-vector estimation. IEEE Signal Processing Letters. https://dx.doi.org/10.1109/LSP.2019.2898775
Schneider, M., & Habets, E. (2019). Iterative DFT-Domain Inverse Filter Optimization Using a Weighted Least-Squares Criterion. IEEE/ACM Transactions on Audio, Speech and Language Processing, 27(12), 1957-1969. https://dx.doi.org/10.1109/TASLP.2019.2936385
Luis Valero, M., & Habets, E. (2019). Low-complexity multi-microphone acoustic echo control in the short-time Fourier transform domain. IEEE/ACM Transactions on Audio, Speech and Language Processing.
Mirabilii, D., & Habets, E. (2019). Multi-channel wind noise reduction using the Corcos model. In Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP). UK.
Chakrabarty, S., & Habets, E. (2019). Multi-speaker DOA estimation using deep convolutional networks trained with noise signals. IEEE Journal of Selected Topics in Signal Processing. https://dx.doi.org/10.1109/JSTSP.2019.2901664
Rummukainen, O., Schlecht, S., Robotham, T., Plinge, A., & Habets, E. (2019). Perceptual exploration of near-field binaural audio rendering in six-degrees-of-freedom virtual reality. In Proc. of the IEEE Conference on Virtual Reality and {3D} User Interfaces. Osaka, Japan.
Schwartz, B., Gannot, S., Habets, E., & Noam, Y. (2019). Recursive expectation-maximization algorithm for dependent observations. IEEE Transactions on Signal Processing.

Last updated on 2016-05-05 at 05:13