Think DSP: Digital Signal Processing in Python by Allen B. Downey pdf free download

Think DSP: Digital Signal Processing in Python by Allen B. Downey pdf free download

Description:

If you understand basic mathematics and know how to program with Python, you’re ready to dive into signal processing. While most resources start with theory to teach this complex subject, this practical book introduces techniques by showing you how they’re applied in the real world. In the first chapter alone, you’ll be able to decompose a sound into its harmonics, modify the harmonics, and generate new sounds.

Author Allen Downey explains techniques such as spectral decomposition, filtering, convolution, and the Fast Fourier Transform. This book also provides exercises and code examples to help you understand the material.

You’ll explore:

  • Periodic signals and their spectrums
  • Harmonic structure of simple waveforms
  • Chirps and other sounds whose spectrum changes over time
  • Noise signals and natural sources of noise
  • The autocorrelation function for estimating pitch
  • The discrete cosine transform (DCT) for compression
  • The Fast Fourier Transform for spectral analysis
  • Relating operations in time to filters in the frequency domain
  • Linear time-invariant (LTI) system theory
  • Amplitude modulation (AM) used in radio

 

Book Description

Digital Signal Processing in Python

About the Author

Allen Downey is a Professor of Computer Science at Olin College of Engineering. He has taught at Wellesley College, Colby College and U.C. Berkeley. He has a Ph.D. in Computer Science from U.C. Berkeley and Master’s and Bachelor’s degrees from MIT.

Biography

Allen Downey is a Professor of Computer Science at Olin College and author of Think Python, Think Stats, Think Bayes, Think Complexity, and several other computer science books. The idea behind these books is that if you know how to program, you can use that skill to learn other things.

<< Think DSP: Digital Signal Processing in Python >>
<< Think DSP: Digital Signal Processing in Python >>

Format: PDF

Size: 2.216 MB

 

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Pattern Recognition and Machine Learning by Christopher M. Bishop pdf free download

Pattern Recognition and Machine Learning by Christopher M. Bishop pdf free download

Description:

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

I do not have a formal math background and I am able to follow this book. It is very easy to understand for a self motivated learner. The highest math class I have taken was Calculus in high school; the rest has been supplemental from various online videos and wikipedia articles. The author provides both the symbolic mathematical explanation as well as the geometric meaning in graphs. This really helps to clarify things. I am learning the math, the techniques, and the intuition behind it as well. This is rare to find all in one source but this book manages to do an excellent job at it.

<< Pattern Recognition and Machine Learning >>

<< Pattern Recognition and Machine Learning >>

Format: PDF

Size: 4.735 MB

 

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Introduction to Digital Filters: with Audio Applications by Julius O. Smith III pdf free download

Introduction to Digital Filters: with Audio Applications by Julius O. Smith III pdf free download

Description:

A digital filter can be pictured as a “black box” that accepts a sequence of numbers and emits a new sequence of numbers. In digital audio signal processing applications, such number sequences usually represent sounds. For example, digital filters are used to implement graphic equalizers and other digital audio effects. This book is a gentle introduction to digital filters, including mathematical theory, illustrative examples, some audio applications, and useful software starting points. The theory treatment begins at the high-school level, and covers fundamental concepts in linear systems theory and digital filter analysis. Various “small” digital filters are analyzed as examples, particularly those commonly used in audio applications. Matlab programming examples are emphasized for illustrating the use and development of digital filters in practice.

This is a great book. I’ve only worked my way about 1/2 way through it so far, but it gives a perfect balance of practical and theoretical background so real engineers can get real work done. For the areas it covers, this book is much more understandable than Oppenheim And Schafer. For the areas it doesn’t cover, it’s still more understandable though a little less informative. :-)

The great thing about this book is that it gives you pointers to real tools and real implementations for getting work done (i.e. lots of matlab examples, c examples, etc).

It’s a bargain at twice the price.

This book is free online, but I referred to it so many times that I decided to buy it. This book has an excellent flow with relevant matlab (or free octave) examples. It starts from the very basic and proceeds at a nice pace. I used it as a review and was very pleased with its contents. Thank you Julius for an excellent book.

Format: PDF

Size: 10.93 MB

 

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