Think DSP Digital Signal Processing in Python 1st edition by Allen Downey – Ebook PDF Instant Download/Delivery: 1491938455, 978-1491938454
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Product details:
ISBN 10: 1491938455
ISBN 13: 978-1491938454
Author: Allen Downey
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
Other books in this series include Think Stats and Think Bayes, also by Allen Downey.
Think DSP Digital Signal Processing in Python 1st Table of contents:
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1. Sounds and Signals
Periodic Signals
Spectral Decomposition
Signals
Reading and Writing Waves
Spectrums
Wave Objects
Signal Objects
Exercises
2. Harmonics
Triangle Waves
Square Waves
Aliasing
Computing the Spectrum
Exercises
3. Non-Periodic Signals
Linear Chirp
Exponential Chirp
Spectrum of a Chirp
Spectrogram
The Gabor Limit
Leakage
Windowing
Implementing Spectrograms
Exercises
4. Noise
Uncorrelated Noise
Integrated Spectrum
Brownian Noise
Pink Noise
Gaussian Noise
Exercises
5. Autocorrelation
Correlation
Serial Correlation
Autocorrelation
Autocorrelation of Periodic Signals
Correlation as Dot Product
Using NumPy
Exercises
6. Discrete Cosine Transform
Synthesis
Synthesis with Arrays
Analysis
Orthogonal Matrices
DCT-IV
Inverse DCT
The Dct Class
Exercises
7. Discrete Fourier Transform
Complex Exponentials
Complex Signals
The Synthesis Problem
Synthesis with Matrices
The Analysis Problem
Efficient Analysis
DFT
The DFT Is Periodic
DFT of Real Signals
Exercises
8. Filtering and Convolution
Smoothing
Convolution
The Frequency Domain
The Convolution Theorem
Gaussian Filter
Efficient Convolution
Efficient Autocorrelation
Exercises
9. Differentiation and Integration
Finite Differences
The Frequency Domain
Differentiation
Integration
Cumulative Sum
Integrating Noise
Exercises
10. LTI Systems
Signals and Systems
Windows and Filters
Acoustic Response
Systems and Convolution
Proof of the Convolution Theorem
Exercises
11. Modulation and Sampling
Convolution with Impulses
Amplitude Modulation
Sampling
Aliasing
Interpolation
Summary
Exercises
Index
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Tags: Allen Downey, Think DSP, Signal Processing