roam/20210219114633-digital_audio_processing.org
2021-07-29 18:51:25 -04:00

1.6 KiB

Digital Audio Processing

Analysis and manipulation of PCM Audio.

Sine Wave

  import numpy as np

  samplerate = 44100
  frequency = 440
  samples = 44100

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

/correlr/roam/media/commit/d28ce1411143f8a8d664a19676644cfe825dd467/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}
  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"

/correlr/roam/media/commit/d28ce1411143f8a8d664a19676644cfe825dd467/dap-volume.png