micStream.start_stream()
while inputStream.is_active():
print("Beginning listening...")
time.sleep(LISTEN_SECONDS)
inputStream.stop_stream()
micStream.stop_stream()
micStream.close()
inputStream.close()
audio.terminate()
print("Finished listening")
# Power data plot
micPowerTimeVals = getTimeValues(RATE, CHUNK, len(micPowerData))
expectedMicPowerTimeVals = getTimeValues(RATE, CHUNK,
len(expectedMicPowerData))
micPowerFig = plt.figure(figsize=(30, 10))
micPowerFig.suptitle('Mic Power & Expected Mic Power over Time',
fontsize=14,
fontweight='bold')
micPowerPlot, = plt.plot(micPowerTimeVals,
micPowerData,
label="Actual Mic Power")
expMicPowerPlot, = plt.plot(expectedMicPowerTimeVals,
expectedMicPowerData,
label="Expected Mic Power")
plt.xlabel('Time (s)')
plt.ylabel('UNITS')
plt.legend(handles=[micPowerPlot, expMicPowerPlot])
def calibrate(plot=False):
# Begin main thread of code
audio = pyaudio.PyAudio()
print("DEFAULT DEVICE: ")
print(audio.get_default_output_device_info())
# FOR MAC - built-in mic has device ID 0, USB Audio device has device ID 2
# FOR PI - input audio has device ID 2, mic audio has device ID 3
# Open input stream source
# Open mic stream souce
micStream = audio.open(format=FORMAT,
input_device_index=getMicDeviceID(audio),
channels=1,
rate=RATE,
input=True,
output=True,
frames_per_buffer=CHUNK,
stream_callback=mic_callback)
micStream.start_stream()
while micStream.is_active():
print("Beginning calibration signal...")
time.sleep(RECORD_SECONDS)
micStream.stop_stream()
micStream.close()
audio.terminate()
print("Finished listening to calibration signal...")
m, b = np.polyfit(calibratePowerData,
micPowerData[0:len(calibratePowerData)], 1)
# save M and B to calibration_parameters.json
with open('calibration_parameters.json', 'w') as outfile:
json.dump({'M': m, 'B': b}, outfile, sort_keys=True, indent=4)
if plot:
lowerBound = min(calibratePowerData)
upperBound = max(calibratePowerData)
print("Generating graphs...")
x = np.linspace(lowerBound, upperBound)
y = []
for i in range(0, len(x)):
y.append(m * x[i] + b)
micTimeValues = getTimeValues(RATE, CHUNK, len(micPowerData))
# Mic power over time plot
micPowerFig = plt.figure(figsize=(30, 4))
micPowerFig.suptitle('Mic Power over Time',
fontsize=14,
fontweight='bold')
plt.plot(micTimeValues, micPowerData)
plt.xlabel('Time (s)')
plt.ylabel('UNITS')
calibrateTimeValues = getTimeValues(RATE, CHUNK,
len(calibratePowerData))
# Calibrate signal power over time plot
calibratePowerFig = plt.figure(figsize=(30, 4))
calibratePowerFig.suptitle('Calibrate Signal Power over Time',
fontsize=14,
fontweight='bold')
plt.plot(calibrateTimeValues, calibratePowerData)
plt.xlabel('Time (s)')
plt.ylabel('UNITS')
# Mic Pickup Power vs. Input Signal Power graph
powerFig = plt.figure(figsize=(30, 4))
powerFig.suptitle('Mic Pickup Power vs. Input Signal Power',
fontsize=14,
fontweight='bold')
powerDataPoints, = plt.plot(calibratePowerData,
micPowerData[0:len(calibratePowerData)],
'o',
color="orange",
label="Power Data Points")
bestFit, = plt.plot(x,
y,
'b-',
label=("Best Fit Line\ny=%.4fx+%.4f" % (m, b)))
plt.xlabel('UNITS')
plt.ylabel('UNITS')
plt.legend(handles=[powerDataPoints, bestFit])
print("Done!")
plt.show()
return (m, b)
def calibrate(plot=False):
os.system(
"amixer set PCM 80%"
) # set the internal pi volume control to 35 (somehow 74% results in 35)
audio = pyaudio.PyAudio()
# FOR MAC - built-in mic has device ID 0, USB Audio device has device ID 2
# FOR PI - input audio has device ID 2, mic audio has device ID 3
# Open input stream source
# Open mic stream souce
micStream = audio.open(format=MIC_FORMAT,
input_device_index=getMicDeviceID(audio),
channels=1,
rate=RATE,
input=True,
frames_per_buffer=CHUNK,
stream_callback=mic_callback)
inputStream = audio.open(format=INPUT_FORMAT,
input_device_index=getInputDeviceID(audio),
channels=CHANNELS,
rate=RATE,
input=True,
frames_per_buffer=CHUNK,
stream_callback=input_callback)
micStream.start_stream()
inputStream.start_stream()
sd.play(cal_array, cal_fs)
sd.wait()
micStream.close()
audio.terminate()
print("Finished listening to calibration signal...")
'''
for i in range(len(micIntensityData)):
micIntensityData[i] = micIntensityData[i]*0.65
'''
# get linear relationship... get min length first so dimensions match in the linear fit
minLength = min(len(calibrateIntensityData), len(micIntensityData))
#scaledDownMinLength = int(0.9*minLength)
r = np.corrcoef(calibrateIntensityData[0:minLength],
micIntensityData[0:minLength])[1:0]
print("\nr = %s\n" % (str(r)))
m, b = np.polyfit(calibrateIntensityData[0:minLength],
micIntensityData[0:minLength], 1)
#m *= 1.1
# save M and B to calibration_parameters.json
with open('calibration_parameters.json', 'w') as outfile:
json.dump({'M': m, 'B': b}, outfile, sort_keys=True, indent=4)
if plot:
lowerBound = min(calibrateIntensityData)
upperBound = max(calibrateIntensityData)
print("Generating graphs...")
x = np.linspace(lowerBound, upperBound)
y = []
for i in range(0, len(x)):
y.append(m * x[i] + b)
micTimeValues = getTimeValues(RATE, CHUNK, len(micIntensityData))
calibrateTimeValues = getTimeValues(RATE, CHUNK,
len(calibrateIntensityData))
"""
# Mic power over time plot
micIntensityFig = plt.figure(figsize=(30,4))
micIntensityFig.suptitle('Mic Intensity over Time', fontsize=14, fontweight='bold')
plt.plot(micTimeValues, micIntensityData)
plt.xlabel('Time (s)')
plt.ylabel('Relative Fraction of Maximum Intensity')
# Calibrate signal power over time plot
calibrateIntensityFig = plt.figure(figsize=(30,4))
calibrateIntensityFig.suptitle('Calibrate Signal Intensity over Time', fontsize=14, fontweight='bold')
plt.plot(calibrateTimeValues, calibrateIntensityData)
plt.xlabel('Time (s)')
plt.ylabel('Relative Fraction of Maximum Intensity')
"""
# Mic Pickup Power vs. Input Signal Power graph
powerFig = plt.figure(figsize=(18, 6))
powerFig.suptitle('Mic Pickup Intensity vs. Input Signal Intensity',
fontsize=14,
fontweight='bold')
powerDataPoints, = plt.plot(calibrateIntensityData[0:minLength],
micIntensityData[0:minLength],
'o',
color="orange",
label="Intensity Data Points")
bestFit, = plt.plot(x,
y,
'b-',
label=("Best Fit Line\ny=%.4fx+%.4f" % (m, b)))
plt.xlabel('Relative Fraction of Maximum Intensity')
plt.ylabel('Relative Fraction of Maximum Intensity')
plt.legend(handles=[powerDataPoints, bestFit])
intensitiesFig = plt.figure(figsize=(18, 12))
intensitiesFig.suptitle('Calibration Graph',
fontsize=14,
fontweight='bold')
# First plot mic
plt.subplot(211)
plt.plot(micTimeValues, micIntensityData, color="tab:blue")
plt.ylabel('Microphone Pickup Intensity')
# Now plot the input on a separate Subplot
plt.subplot(212)
plt.plot(calibrateTimeValues,
calibrateIntensityData,
color="tab:orange")
plt.xlabel('Time (s)')
plt.ylabel('System Input Intensity')
print("Done!")
intensitiesFig.savefig('./plots/c_mic_and_input_intensity.png')
powerFig.savefig('./plots/c_calibration_graph.png')
#plt.show()
resetCalibrateData()
return (m, b)
print("Finished calibrating...")
micStream.close()
audio.terminate()
print("Generating graphs...")
lowerBound = min(calibratePowerData)
upperBound = max(calibratePowerData)
m, b = np.polyfit(calibratePowerData,
micPowerData[0:len(calibratePowerData)], 1)
x = np.linspace(lowerBound, upperBound)
y = []
for i in range(0, len(x)):
y.append(m * x[i] + b)
micTimeValues = getTimeValues(RATE, CHUNK, len(micPowerData))
# Mic power over time plot
micPowerFig = plt.figure(figsize=(30, 4))
micPowerFig.suptitle('Mic Power over Time', fontsize=14, fontweight='bold')
plt.plot(micTimeValues, micPowerData)
plt.xlabel('Time (s)')
plt.ylabel('UNITS')
calibrateTimeValues = getTimeValues(RATE, CHUNK, len(calibratePowerData))
# Calibrate signal power over time plot
calibratePowerFig = plt.figure(figsize=(30, 4))
calibratePowerFig.suptitle('Calibrate Signal Power over Time',
fontsize=14,
fontweight='bold')
plt.plot(calibrateTimeValues, calibratePowerData)
plt.xlabel('Time (s)')
def listen(cal_slope, cal_intercept, sensitivity):
# set the M and B parameters of the calibration graph
global M,B, LISTEN_ACTIVE
#M = cal_slope*1.1
M = cal_slope
B = cal_intercept
SENSITIVITY = sensitivity
CHUNK = sensitivityMap[SENSITIVITY]
print("Set the system's sensitivity to %s\n" % (SENSITIVITY))
# Begin main thread of code
audio = pyaudio.PyAudio()
segment.begin()
# FOR MAC - built-in mic has device ID 0, USB Audio device has device ID 2
# FOR PI - input audio has device ID 2, mic audio has device ID 3
# Open input stream source
inputStream = audio.open(format=INPUT_FORMAT,
input_device_index=getInputDeviceID(audio),
channels=CHANNELS,
rate=RATE,
input=True,
frames_per_buffer=CHUNK,
stream_callback=input_callback)
# Open mic stream souce
micStream = audio.open(format=MIC_FORMAT,
input_device_index=getMicDeviceID(audio),
channels=CHANNELS,
rate=RATE,
input=True,
frames_per_buffer=CHUNK,
stream_callback=mic_callback)
print("Beginning listening...")
inputStream.start_stream()
micStream.start_stream()
LISTEN_ACTIVE = True
# run until LISTEN_ACTIVE is set false by stopListening
while LISTEN_ACTIVE:
pass
inputStream.stop_stream()
micStream.stop_stream()
micStream.close()
inputStream.close()
audio.terminate()
print("Finished listening")
# Power data plot
micPowerTimeVals = getTimeValues(RATE, CHUNK, len(micPowerData))
expectedMicPowerTimeVals = getTimeValues(RATE/CHUNK, 1, len(expectedMicPowerData))
inputPowerTimeVals = getTimeValues(RATE/CHUNK, 1, len(inputPowerData))
micPowerFig = plt.figure(figsize=(18,6))
micPowerFig.suptitle('Mic Power & Expected Mic Power over Time', fontsize=14, fontweight='bold')
micPowerPlot, = plt.plot(micPowerTimeVals,
micPowerData,
label="Actual Mic Power")
expMicPowerPlot, = plt.plot(expectedMicPowerTimeVals,
expectedMicPowerData,
label="Expected Mic Power")
inputPowerPlot, = plt.plot(inputPowerTimeVals,
inputPowerData,
label="Input Power")
plt.xlabel('Time (s)')
plt.ylabel('UNITS')
plt.legend(handles=[micPowerPlot, expMicPowerPlot, inputPowerPlot])
# Plot of moving average of power
avgMicPowerTimeVals = getTimeValues(RATE, CHUNK, len(avgMicPowerData))
avgExpectedMicPowerTimeVals = getTimeValues(RATE, CHUNK, len(avgExpectedMicPowerData))
movingAvgFig = plt.figure(figsize=(18,6))
movingAvgFig.suptitle('Moving Averages of Mic Power & Expected Mic Power over Time', fontsize=14, fontweight='bold')
avgMicPowerPlot, = plt.plot(avgMicPowerTimeVals,
avgMicPowerData,
label="Mic Power Moving Average")
avgExpMicPowerPlot, = plt.plot(avgExpectedMicPowerTimeVals,
avgExpectedMicPowerData,
label="Expected Mic Power Moving Average")
plt.xlabel('Time (s)')
plt.ylabel('UNITS')
plt.legend(handles=[avgMicPowerPlot, avgExpMicPowerPlot])
# Plot of scale factor & ratio over time on same plot
scaleTimeVals = getTimeValues(RATE, CHUNK, len(scaleData))
ratioTimeVals = getTimeValues(RATE, CHUNK, len(ratioData))
potTimeVals = getTimeValues(RATE, CHUNK, len(potValData))
scaleRatioFig = plt.figure(figsize=(18,18))
scaleRatioFig.suptitle('Scale Factor and Avg. Expected Mic Power to Avg Mic Power Ratio over Time', fontsize=14, fontweight='bold')
# First plot ratio
plt.subplot(311)
plt.plot(ratioTimeVals, ratioData, color="tab:red")
plt.ylabel('Ratio Magnitude')
# Next plot a line on the threshold
threshLine = []
for i in range (0, len(ratioTimeVals)):
threshLine.append(THRESHOLD)
plt.plot(ratioTimeVals, threshLine, color="gray", linestyle='dashed')
# Now plot the scale on a separate Subplot
plt.subplot(312)
plt.plot(scaleTimeVals, scaleData, color="tab:blue")
plt.xlabel('Time (s)')
plt.ylabel('Scale Magnitude')
plt.subplot(313)
plt.plot(potTimeVals, potValData, color="tab:green")
plt.xlabel('Time (s)')
plt.ylabel('Potentiometer Value Magnitude')
# plt.show()
micPowerFig.savefig('./plots/l_intensities_over_time.png')
movingAvgFig.savefig('./plots/l_moving_avg.png')
scaleRatioFig.savefig('./plots/l_ratio_etc.png')
resetListenData()