  Human and/or asset tracking using an attached sensor units helps understand
their activities. Most common indoor localization methods for human tracking
technologies require expensive infrastructures, deployment and maintenance. To
overcome this problem, environmental sounds have been used for
infrastructure-free localization. While they achieve room-level classification,
they suffer from two problems: low signal-to-noise-ratio (SNR) condition and
non-uniqueness of sound over the coverage area. A microphone localization
method was proposed using supervised spectral decomposition and spatial
likelihood to solve these problems. The proposed method was evaluated with
actual recordings in an experimental room with a size of 12 x 30 m. The results
showed that the proposed method with supervised NMF was robust under low-SNR
condition compared to a simple feature (mel frequency cepstrum coefficient:
MFCC). Additionally, the proposed method could be easily integrated with prior
distribution, which is available from other Bayesian localizations. The
proposed method can be used to evaluate the spatial likelihood from
environmental sounds.
