  Designing appropriate features for acoustic event recognition tasks is an
active field of research. Expressive features should both improve the
performance of the tasks and also be interpret-able. Currently, heuristically
designed features based on the domain knowledge requires tremendous effort in
hand-crafting, while features extracted through deep network are difficult for
human to interpret. In this work, we explore the experience guided learning
method for designing acoustic features. This is a novel hybrid approach
combining both domain knowledge and purely data driven feature designing. Based
on the procedure of log Mel-filter banks, we design a filter bank learning
layer. We concatenate this layer with a convolutional neural network (CNN)
model. After training the network, the weight of the filter bank learning layer
is extracted to facilitate the design of acoustic features. We smooth the
trained weight of the learning layer and re-initialize it in filter bank
learning layer as audio feature extractor. For the environmental sound
recognition task based on the Urban- sound8K dataset, the experience guided
learning leads to a 2% accuracy improvement compared with the fixed feature
extractors (the log Mel-filter bank). The shape of the new filter banks are
visualized and explained to prove the effectiveness of the feature design
process.
