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.
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A large-scale reference dataset for bioacoustics. MeerKAT is a 1068h large-scale dataset containing data from audio-recording collars worn by free-ranging meerkats (Suricata suricatta) at the Kalahari Research Centre, South Africa, of which 184h are labeled with twelve time-resolved vocalization-type ground truth target classes, each with millisecond resolution. The labeled 184h MeerKAT subset exhibits realistic sparsity conditions for a bioacoustic dataset (96% background-noise or other signals and 4% vocalizations), dispersed across 66398 10-second samples, spanning 251562 labeled events and showcasing significant spectral and temporal variability, making it the first large-scale reference point with real-world conditions for benchmarking pretraining and finetune approaches in bioacoustics deep learning.
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This dataset is based on the Spiking Heidelberg Digits (SHD) dataset. Sample inputs consist of two spike encoded digits sampled uniformly at random from the SHD dataset and concatenated, with the target being the sum of the digits (irrespective of language). The train and test split remain the same, with the test set consisting of 16k such samples based on the SHD test set.
Mudestreda Multimodal Device State Recognition Dataset obtained from real industrial milling device with Time Series and Image Data for Classification, Regression, Anomaly Detection, Remaining Useful Life (RUL) estimation, Signal Drift measurement, Zero Shot Flank Took Wear, and Feature Engineering purposes.
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