  We present a system for localizing sound sources in a room with several
microphone arrays. Unlike most existing approaches, the positions of the arrays
in space are assumed to be unknown. Each circular array performs direction of
arrival (DOA) estimation independently. The DOAs are then fed to a fusion
center where they are concatenated and used to perform the localization based
on two proposed methods, which require only few labeled source locations for
calibration. The first proposed method is based on principal component analysis
(PCA) of the observed DOA and does not require any calibration. The array
cluster can then perform localization on a manifold defined by the PCA of
concatenated DOAs over time. The second proposed method performs localization
using an affine transformation between the DOA vectors and the room manifold.
The PCA approach has fewer requirements on the training sequence, but is less
robust to missing DOAs from one of the arrays. The approach is demonstrated
with a set of five 8-microphone circular arrays, placed at unknown fixed
locations in an office. Both the PCA approach and the direct approach can
easily map out a rectangle based on a few calibration points with similar
accuracy as calibration points. The methods demonstrated here provide a step
towards monitoring activities in a smart home and require little installation
effort as the array locations are not needed.
