@InProceedings{8903035,
  author = {A. Brendel and I. Altmann and W. Kellermann},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {Acoustic Source Position Estimation Based On Multi-Feature Gaussian Processes},
  year = {2019},
  pages = {1-5},
  abstract = {Gaussian Processes, representing a Bayesian frame-work for regression, were already previously shown to allow effective range estimation in highly reverberant and noisy scenarios from a single pair of microphones when using the Coherent-to-Diffuse Power Ratio as a feature. In this work we investigate how Gaussian Process regression can jointly estimate range and Direction of Arrival by using the Coherent-to-Diffuse Power Ratio and an additional Direction of Arrival estimation feature (e.g., MUSIC) to achieve an estimate of the source position, based on a single concentrated array requiring only two sensors as a minimum.},
  keywords = {acoustic signal processing;Bayes methods;direction-of-arrival estimation;Gaussian processes;regression analysis;reverberation;acoustic source position estimation;multifeature Gaussian Processes;Bayesian frame-work;effective range estimation;highly reverberant scenarios;noisy scenarios;Coherent-to-Diffuse Power Ratio;Gaussian Process regression;estimate range;additional Direction;Arrival estimation feature;single concentrated array;Estimation;Direction-of-arrival estimation;Microphones;Acoustics;Training;Multiple signal classification;Gaussian process regression;acoustic source localization},
  doi = {10.23919/EUSIPCO.2019.8903035},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570528006.pdf},
}

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