Robust Sound Recognition: A Neuromorphic Approach

Jibin Wu, Zihan Pan, Malu Zhang, Rohan Kumar Das, Yansong Chua, Haizhou Li


Humans perform remarkably well at sound classification that is used as cues to support high-level cognitive functions. Inspired by the anatomical structure of human cochlea and auditory attention mechanism, we present a novel neuromorphic sound recognition system that integrates an event-driven auditory front-end and a biologically plausible spiking neural network classifier (SNN) for robust sound and speech recognition. Due to its event-driven nature, the SNN classifier is several orders of magnitude more energy efficient than deep learning classifier, therefore, it is suitable for many applications in wearable devices.


Cite as: Wu, J., Pan, Z., Zhang, M., Das, R.K., Chua, Y., Li, H. (2019) Robust Sound Recognition: A Neuromorphic Approach. Proc. Interspeech 2019, 3667-3668.


@inproceedings{Wu2019,
  author={Jibin Wu and Zihan Pan and Malu Zhang and Rohan Kumar Das and Yansong Chua and Haizhou Li},
  title={{Robust Sound Recognition: A Neuromorphic Approach}},
  year=2019,
  booktitle={Proc. Interspeech 2019},
  pages={3667--3668}
}