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Set of 1D CNN models to classify sound clips from the Urban Sound Classification dataset using Keras and Librosa

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DeepLearn_UrbanSoundClassification

Using 1D CNN variants to classify sound files

This dataset contains 1302 labeled sound recordings. Each recording is labeled with the start and end times of sound events from 10 classes: air_conditioner, car_horn, children_playing, dog_bark, drilling, enginge_idling, gun_shot, jackhammer, siren, and street_music. Each recording may contain multiple sound events, but for each file only events from a single class are labeled. The classes are drawn from the urban sound taxonomy.

The dataset can be downloaded from here: https://urbansounddataset.weebly.com/urbansound.html

For our experiment we will use the python library - librosa - to extract features from the sound files and then use 1D CNNs to classify the sounds.

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Set of 1D CNN models to classify sound clips from the Urban Sound Classification dataset using Keras and Librosa

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