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. |
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algorithm Aliasing Amplitude Modulation amps2 analyze2 args autocorrelation function Brownian noise chapter Complex Exponentials complex numbers complex signal complex sinusoid compute the DFT Computing the Spectrum contains Convolution Theorem cumulative sum differentiation Discrete Cosine Transform Discrete Fourier Transform domain corresponds elements evaluate the signal example Exponential Chirp Facebook Figure floatingpoint frame rate freqs frequency components frequency domain Gaussian GitHub harmonics Here’s impulse response impulse train input integration inverse DFT lowpass filter make_wave matrix moving average multiply nonperiodic np.arange(N NumPy NumPy array orthogonal output Periodic Signals phase offset pink noise pitch Python ratio Real Signals repository return ys sawtooth wave scaled copies segment self.amp serial correlation shifted shows the result Signals and Systems smoothing sound Spectral Decomposition spectrogram square wave Systems and Convolution Triangle Waves Uncorrelated Noise UU noise values vector violin wave array Wave object wave.make_spectrum waveform white noise