This project was carried out as a Global AI Hub Deep Learning Bootcamp graduation project. The dataset was taken from the website https://urbansounddataset.weebly.com/urbansound8k.html. This dataset contains 8732 labeled audio excerpts of 10 different classes of sounds, namely air_conditioner, car_horn, children_playing, dog_bark, drill, enginge_idling, gun_shot, jackhammer, siren and street_music classes.
In process preparation; audio files were converted to images with the spectogram function and converted to grayscale. The scale of the grayscale image files is all synchronized and normalized. After the normalization, the data set was recorded as binary in matrices and the data set was obtained in the size of the input data [8732, 16384] and the output data in the size of [8732,] and the model work was started. The model was trained using the Convolutional Neural Network model and results were obtained in the metrics of accuracy: 93.28%, validation accuracy: 86.07%, loss: 0.1979, validation loss: 0.5528. Test results are loss: 0.5320 - accuracy: 85.00%
Justin Salamon*^, Christopher Jacoby* and Juan Pablo Bello*
- Music and Audio Research Lab (MARL), New York University, USA ^ Center for Urban Science and Progress (CUSP), New York University, USA http://serv.cusp.nyu.edu/projects/urbansounddataset http://marl.smusic.nyu.edu/ http://cusp.nyu.edu/
Version 1.0
This dataset contains 8732 labeled sound excerpts (<=4s) of urban sounds from 10 classes: air_conditioner, car_horn, children_playing, dog_bark, drilling, engine_idling, gun_shot, jackhammer, siren, and street_music. The classes are drawn from the urban sound taxonomy described in the following article, which also includes a detailed description of the dataset and how it was compiled:
J. Salamon, C. Jacoby and J. P. Bello, "A Dataset and Taxonomy for Urban Sound Research", 22nd ACM International Conference on Multimedia, Orlando USA, Nov. 2014.
All excerpts are taken from field recordings uploaded to www.freesound.org. The files are pre-sorted into ten folds (folders named fold1-fold10) to help in the reproduction of and comparison with the automatic classification results reported in the article above.
In addition to the sound excerpts, a CSV file containing metadata about each excerpt is also provided.
8732 audio files of urban sounds (see description above) in WAV format. The sampling rate, bit depth, and number of channels are the same as those of the original file uploaded to Freesound (and hence may vary from file to file).
UrbanSound8k.csv
This file contains meta-data information about every audio file in the dataset. This includes:
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slice_file_name: The name of the audio file. The name takes the following format: [fsID]-[classID]-[occurrenceID]-[sliceID].wav, where: [fsID] = the Freesound ID of the recording from which this excerpt (slice) is taken [classID] = a numeric identifier of the sound class (see description of classID below for further details) [occurrenceID] = a numeric identifier to distinguish different occurrences of the sound within the original recording [sliceID] = a numeric identifier to distinguish different slices taken from the same occurrence
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fsID: The Freesound ID of the recording from which this excerpt (slice) is taken
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start The start time of the slice in the original Freesound recording
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end: The end time of slice in the original Freesound recording
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salience: A (subjective) salience rating of the sound. 1 = foreground, 2 = background.
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fold: The fold number (1-10) to which this file has been allocated.
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classID: A numeric identifier of the sound class: 0 = air_conditioner 1 = car_horn 2 = children_playing 3 = dog_bark 4 = drilling 5 = engine_idling 6 = gun_shot 7 = jackhammer 8 = siren 9 = street_music
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class: The class name: air_conditioner, car_horn, children_playing, dog_bark, drilling, engine_idling, gun_shot, jackhammer, siren, street_music.
When UrbanSound8K is used for academic research, we would highly appreciate it if scientific publications of works partly based on the UrbanSound8K dataset cite the following publication:
J. Salamon, C. Jacoby and J. P. Bello, "A Dataset and Taxonomy for Urban Sound Research", 22nd ACM International Conference on Multimedia, Orlando USA, Nov. 2014.
The creation of this dataset was supported by a seed grant by NYU's Center for Urban Science and Progress (CUSP).
Dataset compiled by Justin Salamon, Christopher Jacoby and Juan Pablo Bello. All files are excerpts of recordings uploaded to www.freesound.org. Please see FREESOUNDCREDITS.txt for an attribution list.
The UrbanSound8K dataset is offered free of charge for non-commercial use only under the terms of the Creative Commons Attribution Noncommercial License (by-nc), version 3.0: http://creativecommons.org/licenses/by-nc/3.0/
The dataset and its contents are made available on an "as is" basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, NYU is not liable for, and expressly excludes, all liability for loss or damage however and whenever caused to anyone by any use of the UrbanSound8K dataset or any part of it.
Please help us improve UrbanSound8K by sending your feedback to: justin.salamon@nyu.edu or justin.salamon@gmail.com In case of a problem report please include as many details as possible.