CENSE-2K
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Created By Félix Gontier and Mathieu Lagrange, LS2N, CNRS, Ecole Centrale Nantes Contact : mathieu.lagrange@cnrs.fr If used for research, please refer to: <pre>@article{gontier2021training, title={Polyphonic training set synthesis improves self-supervised urban sound classification}, author={Félix Gontier and Vincent Lostanlen, and Mathieu Lagrange and Nicolas Fortin and Jean-Francois Petiot and Catherine Lavandier}, journal={The Journal of the Acoustical Society of America}, year={2021}, publisher={Acoustical Society of America} } </pre> CENSE-2k contains 182 monophonic audio samples from the CENSE network of acoustic sensors.<br> One expert annotated these audio samples in terms of four classes: background noise, traffic, voice, and birds.<br> <br> The total duration of the dataset is of the order of 2.4k seconds, i.e., 40 minutes. Each audio samples are cut into one or several 3 seconds parts, each resulting into spectrograms of size 23x29, leading to a dataset of 560 spectrograms. Low volume amorphic background noise recordings is added and the cut audio sample is centered within the 3 seconds if shorter. >> import numpy as np >> s=np.load('CENSEk_train_spectralData.npy') >> print(s.shape) (560, 23, 29) The three dimensions respectively corresponds to the sceneId, the frameId (time), and the spectralId (frequency). >> a=np.load('CENSE-2k_train_annotations.npy') >> print(a.shape) (560, 16, 3) The 3 dimensions corresponds to the sceneId, the frameId (time), the sourceId (traffic, voice, birds). Annotation is provided as a binary indicator of source presence for one second, that is 8 consecutive 125 ms frames with a hop of one frame.
