Dataset: UrbanSound8K
This dataset contains 8732 labeled sound excerpts (<=4s) of urban sounds from 10 classes: air_conditioner, car_horn, children_playing, dog_bark, drilling, enginge_idling, gun_shot, jackhammer, siren, and street_music.
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.
In this study, it is aimed to classify sounds by creating a CNN model.
Top three training results:
(Loss, Accuracy)
Model 1: [0.43070679903030396, 0.8687214851379395]
Model 2: [0.3628617525100708, 0.8926940560340881]
Model 3: [0.43551167845726013, 0.8801369667053223]