- In this blog post, we will learn techniques to classify urban sounds into categories using machine learning.
- Today, we will first see what features can be extracted from sound data and how easy it is to extract such features in Python using open source library called Librosa.
- Fortunately, some researchers published urban sound dataset.
- It contains 8,732 labelled sound clips (4 seconds each) from ten classes: air conditioner, car horn, children playing, dog bark, drilling, engine idling, gunshot, jackhammer, siren, and street music.
- In this dataset, the sound files are in .
This post discuss techniques of feature extraction from sound in Python using open source library Librosa and implements a Neural Network in Tensorflow to categories urban sounds, including car horns, children playing, dogs bark, and more.
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