Datasets › SSC
SSC (Spiking Speech Commands v0.2)
The SSC dataset is a spiking version of the Speech Commands dataset release by Google (Speech Commands). SSC was generated using Lauscher, an artificial cochlea model. The SSC dataset consists of utterances recorded from a larger number of speakers under controlled conditions. Spikes were generated in 700 input channels, and it contains 35 word categories from a large number of speakers.
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 archive 2025-07-28
All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Audio Classification | SSC | Event-SSM Accuracy 88.4 | Scalable Event-by-event Processing of Neuromorphic... | Efficient-Scalable-Machine-Learning/event-ssm | 5 | Compare |
Papers archive 2025-07-28
5 shown of 5 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 7. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models | 1 | 1 | 29 Apr 2024 | ran 15 of 18 samples (3 unverified) |
| Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings | 1 | 1 | 30 Jun 2023 | not harvested |
| Investigating current-based and gating approaches for accurate and energy-efficient spiking recurrent neural networks | 0 | 1 | 15 Sep 2022 | not harvested |
| A surrogate gradient spiking baseline for speech command recognition | 1 | 1 | 22 Aug 2022 | not harvested |
| Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks | 0 | 1 | 12 Mar 2021 | not harvested |
Dataset loaders archive 2025-07-28
No loader listed in the archive.
Tasks archive 2025-07-28
License archive 2025-07-28
Creative Commons Attribution 4.0 International License
Modalities archive 2025-07-28
Languages archive 2025-07-28
Variants archive 2025-07-28
- SSC
1 variant name, as the archive lists them.
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