A phased-based approach to neuromorphic audio recognition

This paper presents two novel feature representations for neuromorphic audio data. Neuromorphic audio data are considered state-of-the-art when precise time responses are needed while also keeping energy-demands to a minimum. The approaches presented here are based on the concept of phased encoding of neuromorphic data to generate feature representations. One of the approaches enhances on this further by utilising an autoencoder to reduce the dimensionality of the feature representation allowing for increased accuracy in noise-rich environments such as industrial shop floors. The approaches are evaluated against other leading audio feature representation methods using a neuromorphic version of the TIDIGITS database and results demonstrate high accuracy for the proposed approach. We also find that the autoencoder-backed method achieves the best performance compared with the other methods as the dimensionality reduction results in a generalised representation of the feature set which is less sensitive when compared to other methods.