  In recent years tremendous efforts have been done to advance the state of the
art for Natural Language Processing (NLP) and audio recognition. However, these
efforts often translated in increased power consumption and memory requirements
for bigger and more complex models. These solutions falls short of the
constraints of IoT devices which need low power, low memory efficient
computation, and therefore they fail to meet the growing demand of efficient
edge computing. Neuromorphic systems have proved to be excellent candidates for
low-power low-latency computation in a multitude of applications. For this
reason we present a neuromorphic architecture, capable of unsupervised auditory
feature recognition. We then validate the network on a subset of Google's
Speech Commands dataset.
