Let's take a look at how to read an audio file and visualize the signal. This will be a good starting point, and it will give us a good understanding about the basic structure of audio signals. Before we start, we need to understand that audio files are digitized versions of actual audio signals. Actual audio signals are complex continuous-valued waves. In order to save a digital version, we sample the signal and convert it into numbers. For example, speech is commonly sampled at 44100 Hz. This means that each second of the signal is broken down into 44100 parts, and the values at these timestamps are stored. In other words, you store a value every 1/44100 seconds. As the sampling rate is high, we feel that the signal is continuous when we listen to it on our media players.
Python Machine Learning Cookbook
By :
Python Machine Learning Cookbook
By:
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
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
The Realm of Supervised Learning
Constructing a Classifier
Predictive Modeling
Clustering with Unsupervised Learning
Building Recommendation Engines
Analyzing Text Data
Speech Recognition
Dissecting Time Series and Sequential Data
Image Content Analysis
Biometric Face Recognition
Deep Neural Networks
Visualizing Data
Index
Customer Reviews