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Building Machine Learning Systems with Python

You're reading from   Building Machine Learning Systems with Python Expand your Python knowledge and learn all about machine-learning libraries in this user-friendly manual. ML is the next big breakthrough in technology and this book will give you the head-start you need.

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Product type Paperback
Published in Jul 2013
Publisher Packt
ISBN-13 9781782161400
Length 290 pages
Edition 1st Edition
Languages
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Table of Contents (20) Chapters Close

Building Machine Learning Systems with Python
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started with Python Machine Learning FREE CHAPTER 2. Learning How to Classify with Real-world Examples 3. Clustering – Finding Related Posts 4. Topic Modeling 5. Classification – Detecting Poor Answers 6. Classification II – Sentiment Analysis 7. Regression – Recommendations 8. Regression – Recommendations Improved 9. Classification III – Music Genre Classification 10. Computer Vision – Pattern Recognition 11. Dimensionality Reduction 12. Big(ger) Data Where to Learn More about Machine Learning Index

Looking at music


A very convenient way to get a quick impression of how the songs of the diverse genres "look" like is to draw a spectrogram for a set of songs of a genre. A spectrogram is a visual representation of the frequencies that occur in a song. It shows the intensity of the frequencies on the y axis in the specified time intervals on the x axis; that is, the darker the color, the stronger the frequency is in the particular time window of the song.

Matplotlib provides the convenient function specgram() that performs most of the under-the-hood calculation and plotting for us:

>>> import scipy
>>> from matplotlib.pyplot import specgram
>>> sample_rate, X = scipy.io.wavfile.read(wave_filename)
>>> print sample_rate, X.shape
22050, (661794,)
>>> specgram(X, Fs=sample_rate, xextent=(0,30))

The wave file we just read was sampled at a sample rate of 22,050 Hz and contains 661,794 samples.

If we now plot the spectrogram for these first 30 seconds...

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