# Signal-to-event encoding parameter selection for multiple event classification with spiking neural networks

## TL;DR

This paper introduces a method to optimize event-based signal encoding for machine learning models, achieving high accuracy with fewer data samples.

## Contribution

A model-agnostic method for selecting optimal signal-to-event encoding parameters using Bayesian optimization and k-NN classification.

## Key findings

- The proposed encoding parameter selection achieves up to 91.2% accuracy with 97.8% fewer samples than traditional methods.
- Spiking neural networks trained on optimized encoding parameters reach up to 94.6% classification accuracy.
- The method is validated using vehicle monitoring data with three encoding schemes.

## Abstract

Event-driven systems can operate either on discrete-time event streams or on analog signals transformed into the event domain by a predefined encoding scheme. This paper studies the problem of optimal event-based signal encoding if data are to be processed by a machine learning model, such as the spiking neural network (SNN). We introduce a method of encoding parameter selection that evaluates a k-Nearest Neighbor (k-NN) classifier operating on a measure of the event stream distance in multiple trials of a Bayesian optimization process. The efficiency of the proposed method is assessed by relating the classification performance with the number of events produced by a signal-to-event encoding scheme. The proposed method is validated for vehicle monitoring sensor data with three event-based encoding schemes: level-crossing encoding, send-on-delta, and leaky integrate-and-fire encoder. The best-performing sets of encoding parameters give an average accuracy of up to 0.912 for the k-NN classification, while producing 97.8% fewer number of samples than for the classical periodic discrete-time signal representation. Additionally, we train the SNN classifiers on data encoded according to the selected sets of parameters, achieving an average classification accuracy of up to 0.946, improving upon the k-NN baseline. This shows that the proposed model-agnostic signal-to-event encoding parameter selection is promising for training sophisticated machine learning models.