Name
Title | ||
|---|---|---|
Semi-Supervised Source Localization on Multiple-Manifolds with Distributed Microphones. |
Abstract | ||
|---|---|---|
The problem of source localization with ad hoc microphone networks in noisy and reverberant enclosures, given a training set of prerecorded measurements, is addressed in this paper. The training set is assumed to consist of a limited number of labelled measurements, attached with corresponding positions, and a larger amount of unlabelled measurements from unknown locations. However, microphone calibration is not required. We use a Bayesian inference approach for estimating a function that maps measurement-based feature vectors to the corresponding positions. The central issue is how to combine the information provided by the different microphones in a unified statistical framework. To address this challenge, we model this function using a Gaussian process with a covariance function that encapsulates both the connections between pairs of microphones and the relations among the samples in the training set. The parameters of the process are estimated by optimizing a maximum likelihood (ML) criterion. In addition, a recursive adaptation mechanism is derived where the new streaming measurements are used to update the model. Performance is demonstrated for 2-D localization of both simulated data and real-life recordings in a variety of reverberation and noise levels. |
Year | Venue | DocType |
|---|---|---|
2016 | CoRR | Journal |
Volume | Citations | PageRank |
abs/1610.04770 | 1 | 0.34 |
References | Authors | |
30 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bracha Laufer-Goldshtein | 1 | 21 | 5.22 |
| Ronen Talmon | 2 | 234 | 27.28 |
| Sharon Gannot | 3 | 1754 | 130.51 |