  In this paper, we propose an effective and robust method for acoustic scene
analysis based on spatial information extracted from partially synchronized
and/or closely located distributed microphones. In the proposed method, to
extract spatial information from distributed microphones while taking into
account whether any pairs of microphones are synchronized and/or closely
located, we derive a new cepstrum feature utilizing a graph-based basis
transformation. Specifically, in the proposed graph-based cepstrum, the
logarithm of the amplitude in a multichannel observation is converted to a
feature vector by an inverse graph Fourier transform, which can consider
whether any pair of microphones is connected. Our experimental results indicate
that the proposed graph-based cepstrum effectively extracts spatial information
with consideration of the microphone connections. Moreover, the results show
that the proposed method more robustly classifies acoustic scenes than
conventional spatial features when the observed sounds have a large
synchronization mismatch between partially synchronized microphone groups.
