  One of the biggest challenges of acoustic scene classification (ASC) is to
find proper features to better represent and characterize environmental sounds.
Environmental sounds generally involve more sound sources while exhibiting less
structure in temporal spectral representations. However, the background of an
acoustic scene exhibits temporal homogeneity in acoustic properties, suggesting
it could be characterized by distribution statistics rather than temporal
details. In this work, we investigated using auditory summary statistics as the
feature for ASC tasks. The inspiration comes from a recent neuroscience study,
which shows the human auditory system tends to perceive sound textures through
time-averaged statistics. Based on these statistics, we further proposed to use
linear discriminant analysis to eliminate redundancies among these statistics
while keeping the discriminative information, providing an extreme com-pact
representation for acoustic scenes. Experimental results show the outstanding
performance of the proposed feature over the conventional handcrafted features.
