def microphone_update(stream):
global y_roll
# Normalize new audio samples
y = np.fromstring(stream.read(samples_per_frame), dtype=np.int16)
y = y / 2.0**15
# Construct a rolling window of audio samples
y_roll = np.roll(y_roll, -1, axis=0)
y_roll[-1, :] = np.copy(y)
y_data = np.concatenate(y_roll, axis=0)
volume.update(np.nanmean(y_data**2))
if volume.value < config.MIN_VOLUME_THRESHOLD:
print('No audio input. Volume below threshold. Volume:', volume.value)
visualize(np.tile(0.0, config.N_PIXELS))
else:
XS, YS = dsp.fft(y_data, window=np.hamming)
# Construct Mel filterbank
YS = YS[XS >= 0.0]
XS = XS[XS >= 0.0]
YS = np.atleast_2d(np.abs(YS)).T * dsp.mel_y.T
YS = np.sum(YS, axis=0)**2.0
mel = np.concatenate((YS[::-1], YS))
mel = interpolate(mel, config.N_PIXELS)
mel = (mel)**2.
mel_gain.update(mel)
mel = mel / mel_gain.value
visualize(mel)
GUI.app.processEvents()
print('FPS {:.0f} / {:.0f}'.format(frames_per_second(), config.FPS))
def microphone_update(stream):
global y_roll, prev_rms, prev_exp
# Retrieve and normalize the new audio samples
try:
y = np.fromstring(stream.read(samples_per_frame), dtype=np.int16)
except IOError:
y = y_roll[config.N_ROLLING_HISTORY - 1, :]
global buffer_overflows
print('Buffer overflows: {0}'.format(buffer_overflows))
buffer_overflows += 1
# Seperates a stereo feed into the left and right streams,
# then pulls out the left channel for further processing
if config.USE_LOOPBACK:
y = np.reshape(y, (int(config.MIC_RATE / config.FPS), 2))
y = y[:, 0]
# Normalize samples between 0 and 1
y = y / 2.0**15
# Construct a rolling window of audio samples
y_roll = np.roll(y_roll, -1, axis=0)
y_roll[-1, :] = np.copy(y)
y_data = np.concatenate(y_roll, axis=0)
volume.update(np.nanmean(y_data**2))
if volume.value < config.MIN_VOLUME_THRESHOLD:
print('No audio input. Volume below threshold. Volume:', volume.value)
led.pixels = np.tile(0, (3, config.N_PIXELS))
led.update()
else:
# Transform audio input into the frequency domain
XS, YS = dsp.fft(y_data, window=np.hamming)
# Remove half of the FFT data because of symmetry
YS = YS[:len(YS) // 2]
XS = XS[:len(XS) // 2]
# Construct a Mel filterbank from the FFT data
mel = np.atleast_2d(np.abs(YS)).T * dsp.mel_y.T
# Scale data to values more suitable for visualization
mel = np.mean(mel, axis=0)
mel = mel**2.0
# Gain normalization
mel_gain.update(np.max(gaussian_filter1d(mel, sigma=1.0)))
mel = mel / mel_gain.value
mel = mel_smoothing.update(mel)
# Map filterbank output onto LED strip
output = visualization_effect(mel)
led.pixels = output
led.update()
if config.USE_GUI:
# Plot filterbank output
x = np.linspace(config.MIN_FREQUENCY, config.MAX_FREQUENCY,
len(mel))
mel_curve.setData(x=x, y=fft_plot_filter.update(mel))
# Plot the color channels
r_curve.setData(y=led.pixels[0])
g_curve.setData(y=led.pixels[1])
b_curve.setData(y=led.pixels[2])
app.processEvents()
if config.DISPLAY_FPS:
print('FPS {:.0f} / {:.0f}'.format(frames_per_second(), config.FPS))
def microphone_update(stream):
global y_roll
# Retrieve new audio samples and construct the rolling window
y = np.fromstring(stream.read(samples_per_frame), dtype=np.int16)
y = y / 2.0**15
y_roll = np.roll(y_roll, -1, axis=0)
y_roll[-1, :] = np.copy(y)
y_data = np.concatenate(y_roll, axis=0)
# # Calculate onset detection functions
# SF, NWPD, RCD = dsp.onset(y_data)
# # Apply Gaussian blur to improve agreement between onset functions
# SF = gaussian_filter1d(SF, 1.0)
# NWPD = gaussian_filter1d(NWPD, 1.0)
# RCD = gaussian_filter1d(RCD, 1.0)
# # Update and normalize peak followers
# SF_peak.update(np.max(SF))
# NWPD_peak.update(np.max(NWPD))
# RCD_peak.update(np.max(RCD))
# SF /= SF_peak.value
# NWPD /= NWPD_peak.value
# RCD /= RCD_peak.value
# # Normalize and update onset spectrum
# onset = SF + NWPD + RCD
# onset_peak.update(np.max(onset))
# onset /= onset_peak.value
# onsets.update(onset)
# # Map the onset values to LED strip pixels
# if len(onsets.value) != config.N_PIXELS:
# onset_values = interpolate(onsets.value, config.N_PIXELS)
# else:
# onset_values = np.copy(onsets.value)
# brightness = led_visualization(onset_values)
XS, YS = dsp.fft(y_data, window=np.hamming)
YS = YS[XS >= 0.0]
XS = XS[XS >= 0.0]
YS = np.atleast_2d(np.abs(YS)).T * dsp.mel_y.T
YS = np.sum(YS, axis=0)**2.0
#YS = gaussian_filter1d(YS, 2.0)
YS = np.diff(YS, n=0)
YS_peak.update(np.max(YS))
YS /= YS_peak.value
if len(YS) != config.N_PIXELS:
YS = interpolate(YS, config.N_PIXELS)
#YS = led_visualization(YS)
YS = led_vis3(YS)
# Plot the onsets
#plot_x = np.array(range(1, len(onsets.value) + 1))
plot_x = np.array(range(1, len(YS) + 1))
#plot_y = [onsets.value**i for i in np.linspace(2.0, 0.25, config.N_CURVES)]
plot_y = [YS]
update_plot_1(plot_x, plot_y)
app.processEvents()
print('FPS {:.0f} / {:.0f}'.format(frames_per_second(), config.FPS))