- AM, 11.2
- Anaconda, 0.2
- Audio widget, 1.5
- abs, 7.1
- acoustic response, 10.3
- aliasing, 2, 2.3, 2.5, 7.9, 11, 11.7
- amplifier, 10.1
- amplitude, 1.2, 1.7, 2.4, 7.1
- amplitude modulation, 11.2
- analog-to-digital converter, 11.3
- analysis, 6, 6.1, 7.5
- angle, 2.4, 7.1, 7.1
- anti-aliasing filter, 11.7
- aplay, 1.4
- autocorrelation, 5, 5.3, 8.7
- autocorrelation function, 5.4
- Bartlett’s method, 4.6
- Binder, 0.2
- BitCoin, 4.6, 5.7
- Boccherini, 1.1
- Brownian noise, 4.1, 4.3, 5.2, 9.1, 9.6
- band stop filter, 1.5
- bias, 4.4, 9.4
- bit depth, 11.3
- boxcar window, 8.5
- brick wall filter, 11.5
- Cartesian coordinate, 2.4
- Convolution Theorem, 8, 8.4, 9.5, 10.4, 10.5
- CosSignal, 1.3
- carrier wave, 11.2
- characterize, 10.1
- chirp, 3.1, 3.9
- circuits, 10.1
- circular convolution, 8.2, 10.6
- clone, 0.2
- complex, 7.1
- complex amplitude, 7.4
- complex conjugate, 7.6
- complex exponential, 7.1, 7.2, 10.5
- complex number, 2.4
- complex plane, 7.1
- complex signal, 7.3
- complex sinusoid, 7.2, 9.3
- component, 1.3, 10.5
- compression, 6.8
- contributors, 0.2
- convolution, 8, 8.2, 10.4, 11.1, 11.2
- correlation, 4.3, 4.4, 5.1, 5.5, 7.8
- correlation matrix, 5.1
- cosine, 1.3
- cross-correlation, 8.2, 8.7
- cubic waveform, 9.7
- cumsum, 3.1, 9.7
- cumulative sum, 3.1, 9.5, 9.6
- cycle, 1.1, 2.1
- Danielson-Lanczos lemma, 7.10
- DCT, 6
- DCT-IV, 6.5
- Dct, 6.7
- DFT, 1.2, 2.1, 6, 7, 7.7, 7.10, 10.5
- demodulation, 11.2
- derivative, 3.1, 9.3
- diff, 9.7
- differential equations, 10.1
- differentiate, 9.7
- differentiation, 9, 9.3
- discontinuity, 3.6
- discrete cosine transform, 6
- discrete Fourier transform, 1.2, 7
- dominant frequency, 1.2
- dot product, 5.5, 6.2
- Euler, 7.1
- Euler’s formula, 7.1
- Eye of Sauron, 3.3
- eigenfunction, 9.3, 10.5
- eigenvalue, 9.3, 10.5
- equal temperament, 1.2
- exponential chirp, 3.2
- Facebook stock price, 8.1, 8.6, 8.7
- Fast Fourier Transform, 1.2, 7.10
- Fast Fourier transform, 2.4
- FFT, 1.2, 8.6
- Freesound, 0.2, 1.8, 4.6, 5.4
- fft, 2.4
- filter, 4.4, 8.4, 9.2, 9.3, 9.4, 10.2
- finite difference, 9.2
- floating-point, 6.5
- folding frequency, 2.3, 7.9
- fork, 0.2
- frame, 1.3
- frequency, 1.1, 1.7, 3.1
- frequency component, 1.3, 6.1, 6.3
- frequency domain, 8.4, 9.2, 11.2
- frequency resolution, 3.5, 5.4
- full DFT, 7.9
- fundamental frequency, 1.2, 2.1
- Gabor limit, 3.5, 5.4
- Gaussian filter, 8.5
- Gaussian noise, 4.1, 4.5
- Gaussian signal, 8.8
- Gaussian window, 8.8, 8.8
- Geiger counter, 4.6
- Git, 0.2
- GitHub, 0.2
- gamma function, 7.1
- glissando, 3.9
- gunshot, 10.3
- Hamming window, 3.7, 8.8
- Hart, Vi, 5.7
- Hertz, 1.1
- harmonic, 1.2, 2.1
- harmonic structure, 2, 2.1, 2.2, 2.5
- harmonics, 7.9
- high pass filter, 1.5
- high-pass filter, 9.1
- identity matrix, 6.4, 7.6
- imaginary number, 7.1
- impulse, 10.1, 10.4, 10.4, 11.1, 11.2
- impulse response, 10.1, 10.6
- impulse train, 11.3
- input, 10.1
- installation, 0.2
- integral, 3.1
- integrate, 9.7
- integrated spectrum, 4.2
- integration, 9, 9.4, 9.5
- interpolation, 11.5
- interval, 3.2
- inverse DCT, 6.6
- inverse DFT, 7.7
- Jupyter, 0.2, 1.5
- LTI, 10.1
- lag, 5.2, 5.3
- leakage, 3.6, 8.1
- linear algebra, 6.2
- linear system, 10.1
- log-log scale, 4.3, 9.6
- logspace, 3.2
- long-range dependence, 5.3
- low pass filter, 1.5
- low-pass filter, 4.4, 8.3, 8.5, 10.2, 11.2
- Montgomery, Chris, 11.7
- magnitude, 2.4, 7.1
- major fifth, 1.2
- major third, 1.2
- matplotlib, 0.2
- matrix, 6.2
- matrix inverse, 6.4
- matrix multiplication, 7.4
- mechanical systems, 10.1
- missing fundamental, 5.7
- modf, 2.1
- modulation, 11.2
- moving average, 8.1
- NaN, 9.4
- NumPy, 0.2, 5.6, 8.2
- Nyquist, 11.5
- Nyquist frequency, 2.3
- nbviewer, 0.2
- noise, 4, 4.6
- non-periodic signal, 3, 9.7
- normal probability plot, 4.5
- normalize, 6.7, 8.7
- octave, 1.2, 3.2
- operator, 9.3
- orthogonal matrix, 6.4, 6.5, 7.6
- outer product, 6.2, 7.4
- output, 10.1
- Pandas, 8.6, 9.1
- Pearson’s correlation, 5.1
- Poisson noise, 4.6
- Python 2, 0.2
- Python 3, 0.2
- padding, 8.1, 8.7, 10.6
- parabola, 9.5
- period, 1.1, 2.2
- periodic, 3.6
- periodic DFT, 7.8
- periodic signal, 1.1, 5.4
- phase, 3.1
- phase offset, 1.3, 1.7, 2.4, 5.1, 6.8, 7.2
- pink noise, 4.1, 4.4, 4.6, 5.2, 8.7
- pitch, 1.1
- pitch estimation, 5.4
- pitch perception, 5.7
- pitch tracking, 5.7
- polar coordinate, 2.4
- power, 4.1, 4.3, 4.6
- power series, 7.1
- power spectrum, 4.3
- proof, 10.5
- random, 4.1, 4.4
- random walk, 4.3
- real DFT, 7.9
- real FFT, 2.4
- red noise, 4.3
- repository, 0.2
- resolution, 3.5
- response, 10.4
- Sampling Theorem, 11, 11.5
- SciPy, 0.2, 6.8
- Shannon, 11.5
- Signal, 1.3, 1.7
- SinSignal, 1.3
- Sinusoid, 1.3, 1.7
- Soft Murmur, 4.6
- Spectrum, 1.5
- STFT, 3.4
- SumSignal, 1.3
- sample, 1.3, 2.3, 10.4
- sampling, 11, 11.3
- sawtooth chirp, 3.9
- sawtooth waveform, 2.5, 9.7, 10.4
- scale, 1.6, 10.4, 10.6, 11.1
- scientific pitch notation, 1.1
- segment, 1.3
- serial correlation, 5.2
- shift, 1.6, 5.2, 8.1, 10.4, 10.6, 11.1
- short-time Fourier transform, 3.4
- sidelobe, 8.5
- signal, 1
- signal processing, 0
- sinc function, 11.5
- sine, 1.3
- sinusoid, 1.1
- smoothing, 8.1, 8.4
- sound, 1
- sparse array, 6.8
- spectral analysis, 6
- spectral decomposition, 1.2, 9.3, 10.1
- spectral leakage, 3.6, 8.1
- spectral method, 9.3
- spectrogram, 3, 3.4, 3.8, 4.6
- spectrum, 1.3, 2.1, 3.3, 4.1, 8.3, 11.3
- square matrix, 6.3
- square waveform, 2.2, 2.5, 8.1
- standard deviation, 5.1
- standardize, 5.6
- static, 4.1
- stock prices, 9.6
- stretch, 1.8
- symmetric, 6.4
- synthesis, 1.8, 6, 6.1, 7.3
- system, 10.1
- temperament, 1.2
- timbre, 1.1
- time domain, 8.4, 9.1
- time resolution, 3.5
- time-invariant system, 10.1
- timestep, 1.3
- transducer, 1
- transfer function, 10.3, 10.4, 10.4
- transpose, 6.4, 7.6
- triangle waveform, 2.1, 2.5, 6.7
- trombone, 3.9
- tuning fork, 1.1
- UG noise, 4.5
- UU noise, 4.1, 5.2
- unbias, 2.1
- uncorrelated noise, 4.1
- uniform noise, 4.1
- unitary matrix, 7.6
- Voss-McCartney algorithm, 4.6
- vector, 6.4
- video, 11.7
- violin, 1.2, 10.3
- virtual machine, 0.2
- vowel, 3.9
- WAV file, 1.4, 11.3
- Wave, 1.3, 1.6
- waveform, 1.1
- white noise, 4.2, 9.1, 10.1
- window, 3.6, 3.7, 8.1, 8.8, 9.2, 10.2
- windowing, 3.7
這是 ThinkDSP (Think DSP: Digital Signal Processing in Python by Allen B. Downey)的中文翻譯。 是一本用 python 學習數位訊號處理的書籍/電子書。 原文連結為 http://greenteapress.com/thinkdsp/html/index.html 原文書也可以買得到。http://amzn.to/1T8U0mR
[超譯]ThinkDSP 名詞索引
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[超譯]ThinkDSP 說明
ThinkDSP 超譯前言 原文連結在此: http://greenteapress.com/thinkdsp/html/index.html 文件版本 1.0.9 Copyright 2012 Allen B. Downey Permission is grant...
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第五章 自相關 Autocorrelation 在前一章我將白噪音的性質視為「不相關」,因為它的每個值都各自獨立,而把布朗噪音的性質視為「相關」,因為它的每個值都與依據前值。在這章我會更精確定義這些詞,並介紹「自相關函數 autocorrelation function」,這...
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第四章 噪音 | Noise 在英文中,noise (噪音)指的是不想要的或是令人不悅的聲音。在訊號處理的領域裡,它有兩個不同的意思: 如同英文的意思,它意指不想要的訊號。如果兩個訊號互相干擾,彼此都會認為是噪音。 噪音也可以被認為是有許多頻率成份的訊號,所以它沒有諧波...
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第二章 諧波 | Harmonics 在這章,我會介紹幾個新的波形,我們會看到他們的頻譜以瞭解他們的諧波結構,那是由一組正弦曲線組合而成。 我也會介紹在數位訊號處理中最重要的現象之一:aliasing。我也會較仔細解釋 Spectrum 類別是怎麼運作的。 這章的程式碼在...
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