We all got exposed to different sounds every day. Like, the sound of car horns, siren and music etc. How about teaching computer to classify such sounds automatically into categories!
In this blog post, we will learn techniques to classify urban sounds into categories using machine learning. Earlier blog posts covered classification problems where data can be easily expressed in vector form. For example, in the textual dataset, each word in the corpus becomes feature and tf-idf score becomes its value. Likewise, in anomaly detection dataset we saw two features “throughput” and “latency” that fed into a classifier. But when it comes to sound, feature extraction is not quite straightforward. 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.
To get started with this tutorial, please make sure you have following tools installed:
- Tensorflow
- Librosa
- Numpy
- Matplotlib
Read the complete tutorial with source code at this link.
