#### Overview of this book

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.
Python Machine Learning Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
The Realm of Supervised Learning
Visualizing Data
Index

## Transforming audio signals into the frequency domain

Audio signals consist of a complex mixture of sine waves of different frequencies, amplitudes, and phases. Sine waves are also referred to as sinusoids. There is a lot of information that is hidden in the frequency content of an audio signal. In fact, an audio signal is heavily characterized by its frequency content. The whole world of speech and music is based on this fact. Before you proceed further, you will need some knowledge about Fourier transforms. A quick refresher can be found at http://www.thefouriertransform.com. Now, let's take a look at how to transform an audio signal into the frequency domain.

### How to do it…

1. Create a new Python file, and import the following package:

import numpy as np
from scipy.io import wavfile
import matplotlib.pyplot as plt
2. Read the input_freq.wav file that is already provided to you:

# Read the input file
sampling_freq, audio = wavfile.read('input_freq.wav')
3. Normalize the signal, as follows:

# Normalize the...