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{"references": ["T. Spadini. (2019). Sound Events for Surveillance Applications (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3519845", "P. Foggia, N. Petkov, A. Saggese, N. Strisciuglio, M. Vento. \"Reliable Detection of Audio Events in Highly Noisy Environments.\" Pattern Recognition Letters, Available online 9 July 2015, ISSN 0167-8655, http://dx.doi.org/10.1016/j.patrec.2015.06.026.", "J. Salamon, C. Jacoby, J. P. Bello, \"A Dataset and Taxonomy for Urban Sound Research\", 22nd ACM International Conference on Multimedia, Orlando USA, Nov 2014, doi: 10.1145/2647868.2655045", "A. Diment, A. Mesaros,T. Heittola, T. Virtanen. (2017). TUT Rare sound events, Development dataset [Data set]. Zenodo. http://doi.org/10.5281/zenodo.401395", "Still North Media libraries. The firearm sound library. Accessed on: Nov. 2, 2020. [Online]. Available: https://www.stillnorthmedia.com/libraries/", "Listis: Rouen Audio scene dataset. Accessed on: Jul. 2, 2020. [Online]. Available: https://sites.google.com/site/alainrakotomamonjy/home/audio-scene", "A. Mesaros, T. Heittola, T. Virtanen. (2017). TUT Acoustic scenes 2017, Development dataset [Data set]. Zenodo. http://doi.org/10.5281/zenodo.400515", "A. Diment, A. Mesaros,T. Heittola, T. Virtanen. (2017). TUT Rare sound events, Development dataset [Data set]. Zenodo. http://doi.org/10.5281/zenodo.401395", "Trowitzsch, J. Taghia,Y. Kashef, K. Obermayer. (2019). NIGENS general sound events database [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2535878", "E. Fonseca, M. Collado, M. Plakal, D. P. W. Ellis, F. Font, X. Favory, X. Serra. (2019). FSDnoisy18k (Version 1.0) [Data set]. Zenodo. doi: http://doi.org/10.5281/zenodo.2529934"]}
The dataset Dataset-AOB is an audio dataset collected and manually edited for urban sounds events classification using Convolutional Neural Networks for the Master Thesis: Ospina, A. "Audio Event Classification using Deep Learning. Use case: Urban Sounds Events classification with Convolutional Neural Networks," M.Eng. thesis, Beuth University of Applied Sciences, Berlin, 2020. - 10 audio events: alarm-siren, children playing, dog bark, engine, footsteps, glass breaking, gun shot, metro train, rain and screams. - duration: < 4 seconds - format: (.wav) - sampling rate: 22KHz - 44KHz - files: Dataset-AOB: development dataset (4831 samples), DatasetEVAL-AOB: evaluation (218 samples) - metadata: (.csv) - sources per class: (.png) Contact: aospinab@gmail.com
audio event classification, urban sounds events, deep learning
audio event classification, urban sounds events, deep learning
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