We discussed earlier how to convert a signal into the frequency domain. In most modern speech recognition systems, people use frequency-domain features. After you convert a signal into the frequency domain, you need to convert it into a usable form. Mel Frequency Cepstral Coefficients (MFCC) is a good way to do this. MFCC takes the power spectrum of a signal and then uses a combination of filter banks and discrete cosine transform to extract features. If you need a quick refresher, you can check out http://practicalcryptography.com/miscellaneous/machine-learning/guide-mel-frequency-cepstral-coefficients-mfccs. Make sure that the python_speech_features
package is installed before you start. You can find the installation instructions at http://python-speech-features.readthedocs.org/en/latest. Let's take a look at how to extract MFCC features.
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