Audio scene monitoring using redundant un-localized microphone arrays

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.