Think DSP: Digital Signal Processing in Python is an introduction to signal processing and system analysis using a computational approach. The premise of this book (like the others in the Think X series) is that if you know how to program, you can use that skill to learn other things. By the end of the first chapter, you'll be able to decompose a sound into its harmonics, modify the harmonics, and generate new sounds. Subsequent chapters follow a logical progression that develops the important ideas incrementally, with a focus on applications.
| Preface |
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1 | (12) |
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1 | (2) |
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3 | (2) |
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5 | (2) |
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Reading and Writing Waves |
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7 | (1) |
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7 | (1) |
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8 | (1) |
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9 | (2) |
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11 | (2) |
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13 | (12) |
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13 | (3) |
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16 | (1) |
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17 | (3) |
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20 | (1) |
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21 | (4) |
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25 | (14) |
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25 | (3) |
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28 | (1) |
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29 | (1) |
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30 | (1) |
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31 | (1) |
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32 | (1) |
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33 | (2) |
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Implementing Spectrograms |
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35 | (4) |
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39 | (14) |
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39 | (3) |
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42 | (1) |
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43 | (3) |
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46 | (2) |
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48 | (2) |
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50 | (3) |
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53 | (12) |
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53 | (3) |
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56 | (1) |
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57 | (1) |
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Autocorrelation of Periodic Signals |
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58 | (3) |
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Correlation as Dot Product |
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61 | (1) |
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62 | (2) |
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64 | (1) |
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6 Discrete Cosine Transform |
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65 | (12) |
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66 | (1) |
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66 | (2) |
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68 | (1) |
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69 | (2) |
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71 | (1) |
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72 | (1) |
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73 | (1) |
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74 | (3) |
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7 Discrete Fourier Transform |
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77 | (14) |
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77 | (2) |
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79 | (1) |
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80 | (2) |
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82 | (2) |
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84 | (1) |
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85 | (1) |
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86 | (1) |
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87 | (1) |
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88 | (2) |
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90 | (1) |
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8 Filtering and Convolution |
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91 | (14) |
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91 | (3) |
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94 | (1) |
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95 | (2) |
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97 | (1) |
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98 | (2) |
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100 | (1) |
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Efficient Autocorrelation |
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101 | (2) |
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103 | (2) |
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9 Differentiation and Integration |
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105 | (14) |
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105 | (2) |
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107 | (1) |
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108 | (3) |
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111 | (1) |
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112 | (3) |
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115 | (1) |
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116 | (3) |
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119 | (14) |
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119 | (2) |
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121 | (1) |
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122 | (3) |
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125 | (3) |
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Proof of the Convolution Theorem |
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128 | (2) |
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130 | (3) |
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11 Modulation and Sampling |
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133 | (16) |
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Convolution with Impulses |
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133 | (1) |
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134 | (3) |
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137 | (3) |
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140 | (3) |
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143 | (3) |
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146 | (1) |
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147 | (2) |
| Index |
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149 | |
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.
