71 lines
1.6 KiB
Org Mode
71 lines
1.6 KiB
Org Mode
:PROPERTIES:
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:ID: 4d9e7aa6-1212-4487-84e7-5f9ac8205585
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:END:
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#+title: Digital Audio Processing
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Analysis and manipulation of [[id:472c309f-d50b-4d3b-86cf-1af7c93db0b5][PCM Audio]].
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* Sine Wave
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#+name: sine-wave
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#+begin_src python :exports code :results silent
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import numpy as np
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samplerate = 44100
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frequency = 440
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samples = 44100
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x = np.arange(samples)
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return 100 * np.sin(2 * np.pi * frequency * x / samplerate)
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#+end_src
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#+begin_src python :exports results :results file :noweb yes
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from matplotlib import pyplot as plt
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def wave():
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<<sine-wave>>
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plt.plot(wave()[:100])
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plt.title("Sine Wave")
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plt.savefig("dap-sinewave.png")
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return "dap-sinewave.png"
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#+end_src
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#+RESULTS:
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[[file:dap-sinewave.png]]
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* Getting volume with audioop
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The power of an audio signal is computed using the *root mean square* of the
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fragment:
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\begin{equation}
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{\displaystyle x_{\text{RMS}}={\sqrt {{\frac {1}{n}}\left(x_{1}^{2}+x_{2}^{2}+\cdots +x_{n}^{2}\right)}}.}
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\end{equation}
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#+begin_src python :noweb yes :results file
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import audioop
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import struct
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from matplotlib import pyplot as plt
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def sinewave():
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<<sine-wave>>
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wave = sinewave()
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chunksize = 441
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chunks = [wave[i : i + chunksize] for i in range(0, len(wave), chunksize)]
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raw_chunks = [struct.pack(f"{chunksize}h", *map(int, chunk)) for chunk in chunks]
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plt.plot(
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[audioop.max(c, 2) for c in raw_chunks],
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label="peak")
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plt.plot(
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[audioop.rms(c, 2) for c in raw_chunks],
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label="rms")
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plt.legend()
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plt.title("Volume")
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plt.savefig("dap-volume.png")
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return "dap-volume.png"
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#+end_src
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#+RESULTS:
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[[file:dap-volume.png]]
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