{"url":"/dataset/ssc","name":"SSC","full_name":"Spiking Speech Commands v0.2","description_markdown":"The SSC dataset is a spiking version of the Speech Commands dataset release by Google [(Speech Commands)](https://paperswithcode.com/dataset/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.\r\n\r\nA 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.\r\n\r\nCramer, 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.","description_withheld":null,"homepage":"https://zenkelab.org/resources/spiking-heidelberg-datasets-shd/","introduced_date":"2019-10-16","introduced_date_note":null,"introduced_by":{"paper":"/paper/the-heidelberg-spiking-datasets-for-the","title":"The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks","first_author":"Benjamin Cramer","url":null},"license":{"name":"Creative Commons Attribution 4.0 International License","url":null},"modalities":[{"name":"Audio","url":"/datasets/modality/audio"}],"tasks":[{"name":"Classification","url":"/task/classification-1","datasets_with_task":"/datasets/task/classification-1"},{"name":"Audio Classification","url":"/task/audio-classification","datasets_with_task":"/datasets/task/audio-classification"},{"name":"Audio Tagging","url":"/task/audio-tagging","datasets_with_task":"/datasets/task/audio-tagging"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["SSC"],"data_loaders":[],"num_papers_in_archive":7,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/audio-classification-on-ssc","task":"Audio Classification","dataset_variant":"SSC","rows":5,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"Event-SSM","paper":"/paper/scalable-event-by-event-processing-of","metrics":{"Accuracy":"88.4"},"code_links":[{"title":"Efficient-Scalable-Machine-Learning/event-ssm","url":"https://github.com/Efficient-Scalable-Machine-Learning/event-ssm"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/scalable-event-by-event-processing-of","title":"Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models","date":"2024-04-29","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":18,"samples_ran":15,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-delays-in-spiking-neural-networks","title":"Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings","date":"2023-06-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/investigating-current-based-and-gating","title":"Investigating current-based and gating approaches for accurate and energy-efficient spiking recurrent neural networks","date":"2022-09-15","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/a-surrogate-gradient-spiking-baseline-for","title":"A surrogate gradient spiking baseline for speech command recognition","date":"2022-08-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/accurate-and-efficient-time-domain","title":"Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks","date":"2021-03-12","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":18,"samples_ran":15,"samples_unverified":3,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}