Feature Representations for Neuromorphic Audio Spike Streams

Frontiers in Neuroscience
Jithendar AnumulaShih-Chii Liu

Abstract

Event-driven neuromorphic spiking sensors such as the silicon retina and the silicon cochlea encode the external sensory stimuli as asynchronous streams of spikes across different channels or pixels. Combining state-of-art deep neural networks with the asynchronous outputs of these sensors has produced encouraging results on some datasets but remains challenging. While the lack of effective spiking networks to process the spike streams is one reason, the other reason is that the pre-processing methods required to convert the spike streams to frame-based features needed for the deep networks still require further investigation. This work investigates the effectiveness of synchronous and asynchronous frame-based features generated using spike count and constant event binning in combination with the use of a recurrent neural network for solving a classification task using N-TIDIGITS18 dataset. This spike-based dataset consists of recordings from the Dynamic Audio Sensor, a spiking silicon cochlea sensor, in response to the TIDIGITS audio dataset. We also propose a new pre-processing method which applies an exponential kernel on the output cochlea spikes so that the interspike timing information is better preserved. The results fro...Continue Reading

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Citations

Apr 13, 2018·Frontiers in Neuroscience·Jyotibdha AcharyaArindam Basu
Dec 5, 2018·Frontiers in Neuroscience·Jibin WuKay Chen Tan
Apr 8, 2020·Frontiers in Neuroscience·Jibin WuKay Chen Tan
Jun 18, 2021·Frontiers in Neural Circuits·Mohammad-Hassan Tayarani-Najaran, Michael Schmuker

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Methods Mentioned

BETA
feature extraction

Software Mentioned

TIDIGITS
TIDIGITS18
CochLP
Tensorflow

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