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Gburrek, Tobias, et al. “Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks.” Proc. Asilomar Conference on Signals, Systems, and Computers, 2023.
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Gburrek, Tobias, et al. “On Synchronization of Wireless Acoustic Sensor Networks in the Presence of Time-Varying Sampling Rate Offsets and Speaker Changes.”10.1109/icassp43922.2022.9746284__ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2022, doi:10.1109/icassp43922.2022.9746284.
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Gburrek, Tobias, et al. “Informed vs. Blind Beamforming in Ad-Hoc Acoustic Sensor Networks for Meeting Transcription.”10.1109/IWAENC53105.2022.9914772__2022 International Workshop on Acoustic Signal Enhancement (IWAENC), IEEE, 2022, doi:10.1109/IWAENC53105.2022.9914772.
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Gburrek, Tobias, et al. A Meeting Transcription System for an Ad-Hoc Acoustic Sensor Network. arXiv, 2022, doi:10.48550/ARXIV.2205.00944.
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Gburrek, Tobias, et al. “Geometry Calibration in Wireless Acoustic Sensor Networks Utilizing DoA and Distance Information.”10.1186/s13636-021-00210-x__EURASIP Journal on Audio, Speech, and Music Processing, 2021, doi:10.1186/s13636-021-00210-x.
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Gburrek, Tobias, et al. “Iterative Geometry Calibration from Distance Estimates for Wireless Acoustic Sensor Networks.”10.1109/icassp39728.2021.9413831__ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, doi:10.1109/icassp39728.2021.9413831.
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Gburrek, Tobias, et al. “On Source-Microphone Distance Estimation Using Convolutional Recurrent Neural Networks.” Speech Communication; 14th ITG-Symposium, 2021, pp. 1–5.
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Chinaev, Aleksej, et al. “Online Estimation of Sampling Rate Offsets in Wireless Acoustic Sensor Networks with Packet Loss.” 29th European Signal Processing Conference (EUSIPCO), 2021, pp. 1–5.
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Gburrek, Tobias, et al. “Deep Neural Network Based Distance Estimation for Geometry Calibration in Acoustic Sensor Network.” European Signal Processing Conference (EUSIPCO), 2020.
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Gburrek, Tobias, et al. “Unsupervised Learning of a Disentangled Speech Representation for Voice Conversion.”10.21437/SSW.2019-15__Proc. 10th ISCA Speech Synthesis Workshop, 2019, pp. 81–86, doi:10.21437/SSW.2019-15.
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