SHD (Spiking Heidelberg Digits)
Introduced by Cramer et al. in The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks
The Spiking Heidelberg Digits (SHD) dataset is an audio-based classification dataset of 1k spoken digits ranging from zero to nine in the English and German languages. The audio waveforms have been converted into spike trains using an artificial model of the inner ear and parts of the ascending auditory pathway. The SHD dataset has 8,156 training and 2,264 test samples. A full description of the dataset and how it was created can be found in the paper below. Please cite this paper if you make use of the dataset.
Cramer, B.; Stradmann, Y.; Schemmel, J.; and Zenke, F. "The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks". IEEE Transactions on Neural Networks and Learning Systems 33, 2744–2757, 2022.
Benchmarks
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SHD
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SNN with Dilated Convolution with Learnable Spacings
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Papers
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