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Building Machine Learning Systems with Python

You're reading from  Building Machine Learning Systems with Python

Product type Book
Published in Jul 2013
Publisher Packt
ISBN-13 9781782161400
Pages 290 pages
Edition 1st Edition
Languages

Table of Contents (20) Chapters

Building Machine Learning Systems with Python
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started with Python Machine Learning 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

Improving classification performance with Mel Frequency Cepstral Coefficients


We have already learned that FFT is pointing us in the right direction, but in itself it will not be enough to finally arrive at a classifier that successfully manages to organize our scrambled directory containing songs of diverse music genres into individual genre directories. We somehow need a more advanced version of it.

At this point, it is always wise to acknowledge that we have to do more research. Other people might have had similar challenges in the past and already found out new ways that might also help us. And indeed, there is even a yearly conference only dedicated to music genre classification organized by the International Society for Music Information Retrieval (ISMIR). Apparently, Automatic Music Genre Classification (AMGC) is an established subfield of Music Information Retrieval (MIR). Glancing over some of the AMGC papers, we see that there is a bunch of work targeting automatic genre classification...

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