Think DSP: Digital Signal Processing in Python"O'Reilly Media, Inc.", 2016. 7. 12. - 168페이지 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:
Other books in this series include Think Stats and Think Bayes, also by Allen Downey. |
목차
Chapter 1 Sounds and Signals | 1 |
Chapter 2 Harmonics | 13 |
Chapter 3 NonPeriodic Signals | 25 |
Chapter 4 Noise | 39 |
Chapter 5 Autocorrelation | 53 |
Chapter 6 Discrete Cosine Transform | 65 |
Chapter 7 Discrete Fourier Transform | 77 |
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자주 나오는 단어 및 구문
algorithm Aliasing amplitude amplitude modulation amps2 analyze2 apply args autocorrelation function Brownian noise chapter chirp complex exponential complex numbers complex sinusoid compute the DFT contains Convolution Theorem convolve corre cosine cumulative sum DFT f differentiation duration elements evaluate the signal example Facebook Fast Fourier Transform Figure Fourier Transform frame rate freqs frequency components frequency domain Frequency Hz Gaussian GitHub harmonics Here's http://tinyurl.com impulse response impulse train input integration inverse DFT ipynb Jupyter linear low-pass filter make_wave matrix modulation moving average multiply notebook np.arange(N NumPy array output periodic signals phase offset pink noise pitch Python ratio repository return ys Safari Books Online sawtooth wave scaled copies segment self.amp sequence shifted shows the result signal processing signal.make_wave smoothing sound spectral spectrogram square wave synthesis problem triangle wave uncorrelated UU noise values vector violin wave array Wave object wave.make_spectrum waveform white noise