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How to convert a spectrogram to 3d plot. Python

I am trying to achieve waterfall graph of wav file. In my attempts I noticed that this is basically a spectogram in 3d (or as close to what I need). I am trying to do this in Python with numpy and matplotlib.

My main problem is I don't know how to change the plot of specgram from matplotlib into a 3d plot.

Sample of my "code":

sample ,data = wavfile.read('file.wav')
F = Figure()
a = F.add_subplot(111,projection='3d') 
Spec, t, freq, im = a.specgram(data,Fs=2)

I've got this far and have no clue what to do next. I wanna change already existing plot into 3d. I have no code of changing it to 3d, due to lack of my knowledge.

Is it possible to convert 2d plot to 3d ? If so how ? Am i better off constructing a new plot with data from specgram?

The desired outcome would be something like the following: desired resultanother desired result Thanks for any replies.

like image 220
John Doe Avatar asked Feb 03 '18 15:02

John Doe


1 Answers

Here are 3D & 2D spectrogram plots of an example signal from scipy that you can find at the end of this page.

enter image description here

enter image description here

from matplotlib import mlab
import matplotlib.pyplot as plt
import numpy as np

# Fixing random state for reproducibility
np.random.seed(666)

title = ('2 Vrms sine wave with modulated frequency around 3kHz, '
         'corrupted by white noise of exponentially decreasing '
         'magnitude sampled at 10 kHz.')

fs = 10e3
N = 1e5
amp = 2 * np.sqrt(2)
noise_power = 0.01 * fs / 2
t = np.arange(N) / float(fs)
mod = 500*np.cos(2*np.pi*0.25*t)
carrier = amp * np.sin(2*np.pi*3e3*t + mod)
noise = np.random.normal(scale=np.sqrt(noise_power), size=t.shape)
noise *= np.exp(-t/5)
y = carrier + noise

def specgram3d(y, srate=44100, ax=None, title=None):
  if not ax:
    ax = plt.axes(projection='3d')
  ax.set_title(title, loc='center', wrap=True)
  spec, freqs, t = mlab.specgram(y, Fs=srate)
  X, Y, Z = t[None, :], freqs[:, None],  20.0 * np.log10(spec)
  ax.plot_surface(X, Y, Z, cmap='viridis')
  ax.set_xlabel('time (s)')
  ax.set_ylabel('frequencies (Hz)')
  ax.set_zlabel('amplitude (dB)')
  ax.set_zlim(-140, 0)
  return X, Y, Z

def specgram2d(y, srate=44100, ax=None, title=None):
  if not ax:
    ax = plt.axes()
  ax.set_title(title, loc='center', wrap=True)
  spec, freqs, t, im = ax.specgram(y, Fs=fs, scale='dB', vmax=0)
  ax.set_xlabel('time (s)')
  ax.set_ylabel('frequencies (Hz)')
  cbar = plt.colorbar(im, ax=ax)
  cbar.set_label('Amplitude (dB)')
  cbar.minorticks_on()
  return spec, freqs, t, im

fig1, ax1 = plt.subplots()
specgram2d(y, srate=fs, title=title, ax=ax1)

fig2, ax2 = plt.subplots(subplot_kw={'projection': '3d'})
specgram3d(y, srate=fs, title=title, ax=ax2)
  
plt.show()

BONUS:

You can listen to the signal by creating a wav file using scipy:

from scipy.io import wavfile
wavfile.write('sig.wav', int(fs), y)
like image 113
OpSocket Avatar answered Oct 05 '22 11:10

OpSocket