CODEWITHSUNDEEP
pythonaudiopython-3.xtrigonometry
twinlakes
twinlakes

Reputation: 10238

Generating vibrato sine wave

I'm trying to create a vibrato by oscillating between two 430Hz and 450Hz, storing the 16-bit sample in the list wav. However, the audible frequency seems to increase range of oscillation across the entire clip. Does anyone know why?

edit: rewrote code to be more clear/concise

# vibrato.py

maxamp = 2**15 - 1 # max signed short
wav = []
(t, dt) = (0, 1 / 44100)
while t < 6.0:
  f = 440 + 10 * math.sin(2 * math.pi * 6 * t)
  samp = maxamp * math.sin(2 * math.pi * f * t)
  wav.append(samp)
  t += dt

--

Update: because the response uses numpy, I'll update my code for plain python3

# vibrato.py

maxamp = 2**15 - 1 # max signed short
wav = []
(t, dt) = (0, 1 / 44100)
phase = 0
while t < 6.0:
  f = 440 + 10 * math.sin(2 * math.pi * 6 * t)
  phase += 2 * math.pi * f * t
  samp = maxamp * math.sin(phase)
  wav.append(samp)
  t += dt

Upvotes: 0

Views: 2891

Answers (1)

tom10
tom10

Reputation: 69192

The issue has to do with an implied phase change that goes along with changing the frequency. In short, when you calculate the response relative to each point in a timeline, it's important to note that the phase of the oscillation will be different for each frequency at each time (except at the starting point where they're all the same). Therefore, moving between frequencies is like moving between different phases. For the case of moving between two distinct frequencies, this can be corrected for post hoc by adjusting the overall signal phases based on the frequency change. I've explained this in another answer so won't explain it again here, but here just show the initial plot that highlights the problem, and how to fix the issue. Here, the main thing added is the importance of a good diagnostic plot, and the right plot for this is a spectrogram.

Here's an example:

import numpy as np

dt = 1./44100
time = np.arange(0., 6., dt)
frequency = 440. - 10*np.sin(2*math.pi*time*1.)  # a 1Hz oscillation
waveform = np.sin(2*math.pi*time*frequency)

Pxx, freqs, bins, im = plt.specgram(waveform, NFFT=4*1024, Fs=44100, noverlap=90, cmap=plt.cm.gist_heat) 
plt.show()

enter image description here

Note that the span of the frequency oscillation is increasing (as you initially heard). Applying the correction linked to above gives:

dt = 1./defaults['framerate']
time = np.arange(0., 6., dt)
frequency = 440. - 10*np.sin(2*math.pi*time*1.)  # a 1Hz oscillation
phase_correction = np.add.accumulate(time*np.concatenate((np.zeros(1), 2*np.pi*(frequency[:-1]-frequency[1:]))))
waveform = np.sin(2*math.pi*time*frequency + phase_correction)

enter image description here Which is much closer to what was intended, I hope.

Another way to conceptualize this, which might make more sense in the context of looping through each time step (as the OP does), and as closer to the physical model, is to keep track of the phase at each step and determine the new amplitude considering both the amplitude and phase from the previous step, and combining these with the new frequency. I don't have the patience to let this run in pure Python, but in numpy the solution looks like this, and gives a similar result:

dt = 1./44100
time = np.arange(0., 6., dt)
f = 440. - 10*np.sin(2*math.pi*time*1.)  # a 1Hz oscillation
delta_phase = 2 * math.pi * f * dt
phase = np.cumsum(delta_phase)  # add up the phase differences along timeline (same as np.add.accumulate)
wav = np.sin(phase)

Upvotes: 4

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