Book Image

Python Machine Learning Cookbook

By : Prateek Joshi, Vahid Mirjalili
Book Image

Python Machine Learning Cookbook

By: Prateek Joshi, Vahid Mirjalili

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.
Table of Contents (19 chapters)
Python Machine Learning Cookbook
About the Author
About the Reviewer

Generating audio signals with custom parameters

We can use NumPy to generate audio signals. As we discussed earlier, audio signals are complex mixtures of sinusoids. So, we will keep this in mind when we generate our own audio signal.

How to do it…

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

    import numpy as np
    import matplotlib.pyplot as plt
    from import write
  2. We need to define the output file where the generated audio will be stored:

    # File where the output will be saved
    output_file = 'output_generated.wav'
  3. Let's specify the audio generation parameters. We want to generate a three-second long signal with a sampling frequency of 44100 and a tonal frequency of 587 Hz. The values on the time axis will go from -2*pi to 2*pi:

    # Specify audio parameters
    duration = 3  # seconds
    sampling_freq = 44100  # Hz
    tone_freq = 587
    min_val = -2 * np.pi
    max_val = 2 * np.pi
  4. Let's generate the time axis and the audio signal. The audio signal is a simple sinusoid with the previously...