@inproceedings{11832,
  abstract     = {{In this paper we propose an approach to retrieve the absolute geometry of an acoustic sensor network, consisting of spatially distributed microphone arrays, from reverberant speech input. The calibration relies on direction of arrival measurements of the individual arrays. The proposed calibration algorithm is derived from a maximum-likelihood approach employing circular statistics. Since a sensor node consists of a microphone array with known intra-array geometry, we are able to obtain an absolute geometry estimate, including angles and distances. Simulation results demonstrate the effectiveness of the approach.}},
  author       = {{Jacob, Florian and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}},
  booktitle    = {{38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)}},
  issn         = {{1520-6149}},
  keywords     = {{Geometry calibration, microphone arrays, position self-calibration}},
  pages        = {{116--120}},
  title        = {{{DoA-Based Microphone Array Position Self-Calibration Using Circular Statistic}}},
  doi          = {{10.1109/ICASSP.2013.6637620}},
  year         = {{2013}},
}

@inproceedings{11833,
  abstract     = {{In this paper we propose an approach to retrieve the geometry of an acoustic sensor network consisting of spatially distributed microphone arrays from unconstrained speech input. The calibration relies on Direction of Arrival (DoA) measurements which do not require a clock synchronization among the sensor nodes. The calibration problem is formulated as a cost function optimization task, which minimizes the squared differences between measured and predicted observations and additionally avoids the existence of minima that correspond to mirrored versions of the actual sensor orientations. Further, outlier measurements caused by reverberation are mitigated by a Random Sample Consensus (RANSAC) approach. The experimental results show a mean positioning error of at most 25 cm even in highly reverberant environments.}},
  author       = {{Jacob, Florian and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}},
  booktitle    = {{International Workshop on Acoustic Signal Enhancement (IWAENC 2012)}},
  keywords     = {{Unsupervised, geometry calibration, microphone arrays, position self-calibration}},
  title        = {{{Microphone Array Position Self-Calibration from Reverberant Speech Input}}},
  year         = {{2012}},
}

@inproceedings{11806,
  abstract     = {{Microphone arrays represent the basis for many challenging acoustic sensing tasks. The accuracy of techniques like beamforming directly depends on a precise knowledge of the relative positions of the sensors used. Unfortunately, for certain use cases manually measuring the geometry of an array is not feasible due to practical constraints. In this paper we present an approach to unsupervised shape calibration of microphone array networks. We developed a hierarchical procedure that first performs local shape calibration based on coherence analysis and then employs SRP-PHAT in a network calibration method. Practical experiments demonstrate the effectiveness of our approach especially for highly reverberant acoustic environments.}},
  author       = {{Hennecke, Marius and Ploetz, Thomas and Fink, Gernot A. and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}},
  booktitle    = {{IEEE/SP 15th Workshop on Statistical Signal Processing (SSP 2009)}},
  keywords     = {{acoustic sensing tasks, array geometry, calibration, coherence analysis, hierarchical procedure, local shape calibration, microphone array networks, microphone arrays, network calibration method, sensor arrays, SRP-PHAT, unsupervised shape calibration}},
  pages        = {{257--260}},
  title        = {{{A hierarchical approach to unsupervised shape calibration of microphone array networks}}},
  doi          = {{10.1109/SSP.2009.5278589}},
  year         = {{2009}},
}