Gurkengewuerz/audio-reactive-led-strip
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Mel visualization is now globally normalized instead of individually normalized.

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Also added realtime adaptive brightness correction to avoid having the
LED strip under or over saturated
This commit is contained in:
Scott Lawson 2016-11-11 15:52:57 -08:00
parent 627f67d3dd
commit cdc4c2976c
1 changed files with 47 additions and 18 deletions
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65
python/mel_visualization.py
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@ -75,24 +75,32 @@ def normalize(f):
return np.floor((f - lmin) / (lmax - lmin) * 255.0) return np.floor((f - lmin) / (lmax - lmin) * 255.0)
# r_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS),
# alpha_decay=0.075, alpha_rise=0.6)
# g_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS),
# alpha_decay=0.25, alpha_rise=0.9)
# b_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS),
# alpha_decay=0.5, alpha_rise=0.95)
r_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS), r_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS),
alpha_decay=0.075, alpha_rise=0.6) alpha_decay=0.1, alpha_rise=0.6)
g_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS), g_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS),
alpha_decay=0.25, alpha_rise=0.9) alpha_decay=0.75, alpha_rise=0.95)
b_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS), b_filt = dsp.ExpFilter(np.tile(0.01, config.N_PIXELS),
alpha_decay=0.5, alpha_rise=0.95) alpha_decay=0.2, alpha_rise=0.4)
def visualize(y): def visualize(y):
y = np.copy(interpolate(y, config.N_PIXELS)) * 255.0 y = np.copy(interpolate(y, config.N_PIXELS)) * 255.0
# Blur the color channels with different strengths # Blur the color channels with different strengths
r = gaussian_filter1d(y, sigma=0.15) r = gaussian_filter1d(y, sigma=1.0)
g = gaussian_filter1d(y, sigma=2.0) g = gaussian_filter1d(y, sigma=0.0)
b = gaussian_filter1d(y, sigma=0.0) b = gaussian_filter1d(y, sigma=0.0)
# Take the geometric mean of the raw and normalized histograms # Take the geometric mean of the raw and normalized histograms
r = np.sqrt(r * normalize(r)) # r = np.sqrt(r * normalize(r))
g = np.sqrt(g * normalize(g)) # g = np.sqrt(g * normalize(g))
b = np.sqrt(b * normalize(b)) # b = np.sqrt(b * normalize(b))
r = np.roll(g, 0)
g = np.roll(g, 0)
b = np.roll(g, 0)
# Update the low pass filters for each color channel # Update the low pass filters for each color channel
r_filt.update(r) r_filt.update(r)
g_filt.update(g) g_filt.update(g)
@ -110,12 +118,17 @@ def visualize(y):
mel_gain = dsp.ExpFilter(np.tile(1e-1, config.N_PIXELS), mel_gain = dsp.ExpFilter(np.tile(1e-1, config.N_PIXELS),
alpha_decay=0.01, alpha_rise=0.99) alpha_decay=0.01, alpha_rise=0.99)
# mel_gain = dsp.ExpFilter(np.tile(1e-1, config.N_PIXELS),
# alpha_decay=0.01, alpha_rise=0.99)
volume = dsp.ExpFilter(config.MIN_VOLUME_THRESHOLD, volume = dsp.ExpFilter(config.MIN_VOLUME_THRESHOLD,
alpha_decay=0.02, alpha_rise=0.02) alpha_decay=0.02, alpha_rise=0.02)
rms = dsp.ExpFilter(0.1, alpha_decay=0.001, alpha_rise=0.001)
exp = dsp.ExpFilter(0.5, alpha_decay=0.001, alpha_rise=0.001)
prev_rms = 1.0
prev_exp = 1.0
def microphone_update(stream): def microphone_update(stream):
global y_roll global y_roll, prev_rms, prev_exp
# Normalize new audio samples # Normalize new audio samples
y = np.fromstring(stream.read(samples_per_frame), dtype=np.int16) y = np.fromstring(stream.read(samples_per_frame), dtype=np.int16)
y = y / 2.0**15 y = y / 2.0**15
@ -137,13 +150,29 @@ def microphone_update(stream):
YS = np.sum(YS, axis=0)**2.0 YS = np.sum(YS, axis=0)**2.0
mel = np.concatenate((YS[::-1], YS)) mel = np.concatenate((YS[::-1], YS))
mel = interpolate(mel, config.N_PIXELS) mel = interpolate(mel, config.N_PIXELS)
mel = (mel)**2. # mel = mel**0.4
mel_gain.update(mel) mel = mel**exp.value
mel_gain.update(np.max(mel))
mel = mel / mel_gain.value mel = mel / mel_gain.value
rms.update(np.sqrt(np.mean(mel**2.0)))
if rms.value > 4e-1:
exp.update(exp.value * 1.2)
elif rms.value < 5e-2:
exp.update(exp.value * 0.8)
rms_delta = '^' if rms.value - prev_rms > 0 else 'v'
exp_delta = '^' if exp.value - prev_exp > 0 else 'v'
print('|{}| {:.0e}, |{}| {:.2}'.format(rms_delta, rms.value, exp_delta, exp.value))
# WHAT IF I TAKE THE TEMPORAL VARIANCE OF EACH INDIVIDUAL BIN
# AND THEN CALCULATE THE COVARIANCE OF HOW THE DIFFERENT BIN VARIANCES
# CHANGE TOGETHER
# COULD COLOR BY COVARIANCE? BLUE PIXELS CHANGE TOGETHER, ETC
prev_exp = exp.value
prev_rms = rms.value
visualize(mel) visualize(mel)
GUI.app.processEvents() GUI.app.processEvents()
print('FPS {:.0f} / {:.0f}'.format(frames_per_second(), config.FPS)) #print('FPS {:.0f} / {:.0f}'.format(frames_per_second(), config.FPS))
# Number of audio samples to read every time frame # Number of audio samples to read every time frame
@ -158,13 +187,13 @@ if __name__ == '__main__':
GUI = gui.GUI(width=800, height=400, title='Audio Visualization') GUI = gui.GUI(width=800, height=400, title='Audio Visualization')
# Audio plot # Audio plot
GUI.add_plot('Color Channels') GUI.add_plot('Color Channels')
r_pen = pg.mkPen((255, 30, 30, 200), width=3) r_pen = pg.mkPen((255, 30, 30, 200), width=6)
g_pen = pg.mkPen((30, 255, 30, 200), width=3) g_pen = pg.mkPen((30, 255, 30, 200), width=6)
b_pen = pg.mkPen((30, 30, 255, 200), width=3) b_pen = pg.mkPen((30, 30, 255, 200), width=6)
GUI.add_curve(plot_index=0, pen=r_pen) GUI.add_curve(plot_index=0, pen=r_pen)
GUI.add_curve(plot_index=0, pen=g_pen) GUI.add_curve(plot_index=0, pen=g_pen)
GUI.add_curve(plot_index=0, pen=b_pen) GUI.add_curve(plot_index=0, pen=b_pen)
GUI.plot[0].setRange(xRange=(0, 60), yRange=(-40, 275)) GUI.plot[0].setRange(xRange=(0, config.N_PIXELS), yRange=(-5, 275))
# Initialize LEDs # Initialize LEDs
led.update() led.update()
# Start listening to live audio stream # Start listening to live audio stream