  The capability for environmental sound recognition (ESR) can determine the
fitness of individuals in a way to avoid dangers or pursue opportunities when
critical sound events occur. It still remains mysterious about the fundamental
principles of biological systems that result in such a remarkable ability.
Additionally, the practical importance of ESR has attracted an increasing
amount of research attention, but the chaotic and non-stationary difficulties
continue to make it a challenging task. In this study, we propose a spike-based
framework from a more brain-like perspective for the ESR task. Our framework is
a unifying system with a consistent integration of three major functional parts
which are sparse encoding, efficient learning and robust readout. We first
introduce a simple sparse encoding where key-points are used for feature
representation, and demonstrate its generalization to both spike and non-spike
based systems. Then, we evaluate the learning properties of different learning
rules in details with our contributions being added for improvements. Our
results highlight the advantages of the multi-spike learning, providing a
selection reference for various spike-based developments. Finally, we combine
the multi-spike readout with the other parts to form a system for ESR.
Experimental results show that our framework performs the best as compared to
other baseline approaches. In addition, we show that our spike-based framework
has several advantageous characteristics including early decision making, small
dataset acquiring and ongoing dynamic processing. Our framework is the first
attempt to apply the multi-spike characteristic of nervous neurons to ESR. The
outstanding performance of our approach would potentially contribute to draw
more research efforts to push the boundaries of spike-based paradigm to a new
horizon.
