NAME¶
spectral analysis - perform realtime spectral analysis
SYNOPSIS¶
numm-run
FILE
DESCRIPTION¶
Frequency makes for a meaningful description of many audio signals. We can use
numpy's fourier analysis to compute spectra from the microphone and display
the results visually. We will break down the process into smaller parts: baby
steps...
First, create and save a skeletal file that moves a line across the screen:
-
idx = 0
def video_out(a):
global idx
a[:,idx] = 255
idx = (idx + 1) % a.shape[1]
def audio_in(a):
pass
Save this snippet and run it with
numm-run.
We will use the
numpy.fft module for our analysis. First we define a
function to get a particular frequency from the fourier transform:
-
import numpy as np
def get_freq(fourier, frequency):
freqs = np.fft.fftfreq(len(fourier), 1/44100.0)
nearest = (abs(freqs - frequency)).argmin()
return abs(fourier[nearest])
Next, we hook up this function to audio input from the microphone. A frequency
bin is chosen on a log scale for each row on the screen to display a
spectogram. In total:
-
import numpy as np
idx = 0
recent_audio = np.zeros(4096, np.int16)
recent_video = np.zeros((240,320,3), np.uint8)
freq_bins = np.exp2(np.linspace(np.log2(27000),np.log2(27),240))
def get_freq(fourier, frequency):
freqs = np.fft.fftfreq(len(fourier), 1/44100.0)
nearest = (abs(freqs - frequency)).argmin()
return abs(fourier[nearest])
def video_out(a):
global idx
fourier=np.fft.fft(recent_audio)
values =np.array([get_freq(fourier,X) for X in freq_bins])
recent_video[:,idx,1] = (values/10000).clip(0,255)
idx = (idx + 1) % a.shape[1]
a[:] = np.roll(recent_video, -idx, axis=1)
def audio_in(a):
recent_audio[:] = np.roll(recent_audio, len(a))
recent_audio[:len(a)] = a.mean(axis=1)
SEE ALSO¶
numm-run(1),
numm.getting-started(7),
numm.one-bit-instrument(7)