:PROPERTIES:
:ID:       4d9e7aa6-1212-4487-84e7-5f9ac8205585
:END:
#+title: Digital Audio Processing

Analysis and manipulation of [[id:472c309f-d50b-4d3b-86cf-1af7c93db0b5][PCM Audio]].

* Sine Wave
#+name: sine-wave
#+begin_src python :exports code :results silent
  import numpy as np

  samplerate = 44100
  frequency = 440
  samples = 44100

  x = np.arange(samples)
  return 100 * np.sin(2 * np.pi * frequency * x / samplerate)
#+end_src

#+begin_src python :exports results :results file :noweb yes
  from matplotlib import pyplot as plt

  def wave():
      <<sine-wave>>

  plt.plot(wave()[:100])
  plt.title("Sine Wave")
  plt.savefig("dap-sinewave.png")
  return "dap-sinewave.png"
#+end_src

#+RESULTS:
[[file:dap-sinewave.png]]
* Getting volume with audioop
The power of an audio signal is computed using the *root mean square* of the
fragment:

\begin{equation}
{\displaystyle x_{\text{RMS}}={\sqrt {{\frac {1}{n}}\left(x_{1}^{2}+x_{2}^{2}+\cdots +x_{n}^{2}\right)}}.}
\end{equation}

#+begin_src python :noweb yes :results file
  import audioop
  import struct

  from matplotlib import pyplot as plt


  def sinewave():
      <<sine-wave>>

  wave = sinewave()
  chunksize = 441
  chunks = [wave[i : i + chunksize] for i in range(0, len(wave), chunksize)]
  raw_chunks = [struct.pack(f"{chunksize}h", *map(int, chunk)) for chunk in chunks]

  plt.plot(
      [audioop.max(c, 2) for c in raw_chunks],
      label="peak")
  plt.plot(
      [audioop.rms(c, 2) for c in raw_chunks],
      label="rms")
  plt.legend()
  plt.title("Volume")
  plt.savefig("dap-volume.png")
  return "dap-volume.png"
#+end_src

#+RESULTS:
[[file:dap-volume.png]]