def fileSpectrogramWrapper(wav_file):
if not os.path.isfile(wav_file):
raise Exception("Input audio file not found!")
[fs, x] = audioBasicIO.readAudioFile(wav_file)
x = audioBasicIO.stereo2mono(x)
specgram, TimeAxis, FreqAxis = aF.stSpectogram(x, fs, round(fs * 0.040),
round(fs * 0.040), True)
def fileSpectrogramWrapper(wav_file):
if not os.path.isfile(wav_file):
raise Exception("Input audio file not found!")
[fs, x] = audioBasicIO.readAudioFile(wav_file)
x = audioBasicIO.stereo2mono(x)
specgram, TimeAxis, FreqAxis = aF.stSpectogram(x, fs, round(fs * 0.040),
round(fs * 0.040), True)
def mtCNN_classification(signal, Fs, mtWin, mtStep, SingleFrame_net,
channel_mean, input_transformer, classNamesCNN,
input_size):
mtWin2 = int(mtWin * Fs)
mtStep2 = int(mtStep * Fs)
stWin = 0.040
stStep = 0.005
N = len(signal)
curPos = 0
count = 0
fileNames = []
flagsInd = []
Ps = []
randomString = (''.join(
random.SystemRandom().choice(string.ascii_uppercase + string.digits)
for _ in range(5)))
while (curPos < N):
N1 = curPos
N2 = curPos + mtWin2 + stStep * Fs
if N2 > N:
N2 = N
xtemp = signal[int(N1):int(N2)]
#print xtemp.shape
#print xtemp.shape[0] # get mid-term segment
if xtemp.shape[0] < 8000:
curPos += mtStep2
count += 1
continue
specgram, TimeAxis, FreqAxis = aF.stSpectogram(
xtemp, Fs, round(Fs * stWin), round(Fs * stStep),
False) # compute spectrogram
specgram = cv2.resize(specgram, (input_size, input_size),
interpolation=cv2.INTER_LINEAR)
#specgram = scipy.misc.imresize(specgram, float(input_size) / float(specgram.shape[0]), interp='bilinear') # resize to 227 x 227
if specgram.shape[0] != specgram.shape[1]:
break
#print specgram.shape
#specgram = scipy.misc.imresize(specgram, float(input_size) / float(specgram.shape[0]), interp='bilinear') # resize to 227 x 227
#print specgram.shape
# imSpec = Image.fromarray(np.uint8(matplotlib.cm.jet(specgram)*255)) # create image
curFileName = randomString + "temp_{0:d}.png".format(count)
fileNames.append(curFileName)
#imSpec = rgb2gray(np.uint8(matplotlib.cm.jet(specgram)*255))
imSpec = Image.fromarray(np.uint8(matplotlib.cm.jet(specgram) * 255))
scipy.misc.imsave(curFileName, imSpec)
T1 = time.time()
output_classes, outputP = singleFrame_classify_video(
curFileName, SingleFrame_net, input_transformer, False,
classNamesCNN, input_size)
T2 = time.time()
os.remove(curFileName)
#print T2 - T1
#flagsInd.append(classNamesCNN.index(output_classes[0]))
Ps.append(np.copy(outputP[0]))
#print flagsInd[-1]
curPos += mtStep2
count += 1
return np.array(flagsInd), classNamesCNN, np.array(Ps)
def createSpectrogramFile(x, Fs, fileName, stWin, stStep, label):
specgramOr, TimeAxis, FreqAxis = aF.stSpectogram(x, Fs, round(Fs * stWin),
round(Fs * stStep), False)
specgramOr = filter.medfilt2d(specgramOr, 5)
save_path = "medfilt5_label_" + label + '/'
if not os.path.exists(save_path):
os.mkdir(save_path)
specgram = cv2.resize(specgramOr, (227, 227),
interpolation=cv2.INTER_LINEAR)
im1 = Image.fromarray(np.uint8(matplotlib.cm.jet(specgram) * 255))
scipy.misc.imsave(save_path + fileName, im1)
def createSpectrogramFile(x, Fs, fileName, stWin, stStep):
specgramOr, TimeAxis, FreqAxis = aF.stSpectogram(x, Fs, round(Fs * stWin),
round(Fs * stStep), False)
print specgramOr.shape
if inputs[2] == 'full':
print specgramOr
numpy.save(fileName.replace('.png', '') + '_spectrogram', specgramOr)
else:
#specgram = scipy.misc.imresize(specgramOr, float(227.0) / float(specgramOr.shape[0]), interp='bilinear')
specgram = cv2.resize(specgramOr, (227, 227),
interpolation=cv2.INTER_LINEAR)
im1 = Image.fromarray(numpy.uint8(matplotlib.cm.jet(specgram) * 255))
scipy.misc.imsave(fileName, im1)
def mtCNN_classification(signal, Fs, mtWin, mtStep, RGB_singleFrame_net,
SOUND_mean_RGB, transformer_RGB, classNamesCNN):
mtWin2 = int(mtWin * Fs)
mtStep2 = int(mtStep * Fs)
stWin = 0.020
stStep = 0.015
N = len(signal)
curPos = 0
count = 0
fileNames = []
flagsInd = []
Ps = []
randomString = (''.join(
random.SystemRandom().choice(string.ascii_uppercase + string.digits)
for _ in range(5)))
while (curPos < N): # for each mid-term segment
N1 = curPos
N2 = curPos + mtWin2 + stStep * Fs
if N2 > N:
N2 = N
xtemp = signal[int(N1):int(N2)] # get mid-term segment
specgram, TimeAxis, FreqAxis = aF.stSpectogram(
xtemp, Fs, round(Fs * stWin), round(Fs * stStep),
False) # compute spectrogram
if specgram.shape[0] != specgram.shape[
1]: # TODO (this must be dynamic!)
break
specgram = scipy.misc.imresize(
specgram,
float(227.0) / float(specgram.shape[0]),
interp='bilinear') # resize to 227 x 227
imSpec = Image.fromarray(np.uint8(matplotlib.cm.jet(specgram) *
255)) # create image
curFileName = randomString + "temp_{0:d}.png".format(count)
fileNames.append(curFileName)
scipy.misc.imsave(curFileName, imSpec)
T1 = time.time()
output_classes, outputP = singleFrame_classify_video(
curFileName, RGB_singleFrame_net, transformer_RGB, False,
classNamesCNN)
T2 = time.time()
#print T2 - T1
flagsInd.append(classNamesCNN.index(output_classes[0]))
Ps.append(outputP[0])
#print flagsInd[-1]
curPos += mtStep2
count += 1
return np.array(flagsInd), classNamesCNN, np.array(Ps)
def wave_to_spectrogram(wav: WaveData, window_size, window_step) -> np.ndarray:
"""
Converts the given WAV data into a spectrogram.
:param wav: The WAV data to convert.
:param window_size: The width of the sampling window in samples.
:param window_step: The step the sampling window takes between FFTs.
:return: A spectrogram.
"""
# Convert the WAV data in a Numpy array
wav_array: np.ndarray = np.array(wav.data)
# Get the spectrogram using pyAudioAnalysis
return aFEx.stSpectogram(wav_array, 1, window_size, window_step, False)[0]
def _generate_spectrogram(self, filename):
[Fs, x] = audioBasicIO.readAudioFile(filename)
x = audioBasicIO.stereo2mono(x)
specgram, TimeAxis, FreqAxis = aF.stSpectogram(x, Fs, round(Fs * 0.040), round(Fs * 0.040), False)
return (specgram, TimeAxis, FreqAxis)
x, Fs, 0.050 * Fs, 0.025 * Fs)
plt.subplot(2, 1, 1)
plt.plot(F[0, :])
plt.xlabel('Frame no')
plt.ylabel(f_names[0])
plt.subplot(2, 1, 2)
plt.plot(F[1, :])
plt.xlabel('Frame no')
plt.ylabel(f_names[1])
plt.show()
print("\n\n\n * * * TEST 2 * * * \n\n\n")
[Fs, x] = audioBasicIO.read_audio_file(root_data_path +
"pyAudioAnalysis/data/doremi.wav")
x = audioBasicIO.stereo_to_mono(x)
specgram, TimeAxis, FreqAxis = audioFeatureExtraction.stSpectogram(
x, Fs, round(Fs * 0.040), round(Fs * 0.040), True)
print("\n\n\n * * * TEST 3 * * * \n\n\n")
[Fs, x] = audioBasicIO.read_audio_file(root_data_path +
"pyAudioAnalysis/data/doremi.wav")
x = audioBasicIO.stereo_to_mono(x)
specgram, TimeAxis, FreqAxis = audioFeatureExtraction.stChromagram(
x, Fs, round(Fs * 0.040), round(Fs * 0.040), True)
print("\n\n\n * * * TEST 4 * * * \n\n\n")
aT.featureAndTrain([root_data_path + "SM/speech", root_data_path + "SM/music"],
1.0, 1.0, 0.2, 0.2, "svm", "temp", True)
print("\n\n\n * * * TEST 5 * * * \n\n\n")
[flagsInd, classesAll, acc, CM] = aS.mtFileClassification(
root_data_path + "pyAudioAnalysis/data//scottish.wav",
def recordAudioSegments(BLOCKSIZE,
Fs=16000,
showSpectrogram=False,
showChromagram=False,
recordActivity=False):
midTermBufferSize = int(Fs * BLOCKSIZE)
print "Press Ctr+C to stop recording"
startDateTimeStr = datetime.datetime.now().strftime("%Y_%m_%d_%I:%M%p")
MEAN, STD = loadMEANS("svmMovies8classesMEANS") # load MEAN feature values
pa = pyaudio.PyAudio()
stream = pa.open(format=FORMAT,
channels=1,
rate=Fs,
input=True,
frames_per_buffer=midTermBufferSize)
midTermBuffer = []
curWindow = []
count = 0
global allData
allData = []
energy100_buffer_zero = []
curActiveWindow = numpy.array([])
timeStart = time.time()
while 1:
try:
block = stream.read(midTermBufferSize)
countB = len(block) / 2
format = "%dh" % (countB)
shorts = struct.unpack(format, block)
curWindow = list(shorts)
midTermBuffer = midTermBuffer + curWindow
# copy to midTermBuffer
del (curWindow)
#print len(midTermBuffer), midTermBufferSize
#if len(midTermBuffer) == midTermBufferSize: # if midTermBuffer is full:
if 1:
elapsedTime = (time.time() - timeStart
) # time since recording started
dataTime = (count + 1) * BLOCKSIZE # data-driven time
# TODO
# mtF, _ = aF.mtFeatureExtraction(midTermBuffer, Fs, BLOCKSIZE * Fs, BLOCKSIZE * Fs, 0.050 * Fs, 0.050 * Fs)
# curFV = (mtF - MEAN) / STD
# TODO
allData += midTermBuffer
midTermBuffer = numpy.double(
midTermBuffer) # convert current buffer to numpy array
# Compute spectrogram
if showSpectrogram:
(spectrogram, TimeAxisS, FreqAxisS) = aF.stSpectogram(
midTermBuffer, Fs, 0.020 * Fs,
0.02 * Fs) # extract spectrogram
FreqAxisS = numpy.array(FreqAxisS) # frequency axis
DominantFreqs = FreqAxisS[numpy.argmax(
spectrogram, axis=1
)] # most dominant frequencies (for each short-term window)
maxFreq = numpy.mean(
DominantFreqs) # get average most dominant freq
maxFreqStd = numpy.std(DominantFreqs)
# Compute chromagram
if showChromagram:
(chromagram, TimeAxisC,
FreqAxisC) = aF.stChromagram(midTermBuffer, Fs,
0.020 * Fs,
0.02 * Fs) # get chromagram
FreqAxisC = numpy.array(
FreqAxisC) # frequency axis (12 chroma classes)
DominantFreqsC = FreqAxisC[numpy.argmax(
chromagram, axis=1)] # most dominant chroma classes
maxFreqC = most_common(DominantFreqsC)[
0] # get most common among all short-term windows
# Plot signal window
signalPlotCV = plotCV(
scipy.signal.resample(midTermBuffer + 16000, WidthPlot),
WidthPlot, HeightPlot, 32000)
cv2.imshow('Signal', signalPlotCV)
cv2.moveWindow('Signal', 50, statusHeight + 50)
# Show spectrogram
if showSpectrogram:
iSpec = numpy.array(spectrogram.T * 255, dtype=numpy.uint8)
iSpec2 = cv2.resize(iSpec, (WidthPlot, HeightPlot),
interpolation=cv2.INTER_CUBIC)
iSpec2 = cv2.applyColorMap(iSpec2, cv2.COLORMAP_JET)
cv2.putText(iSpec2, "maxFreq: %.0f Hz" % maxFreq, (0, 11),
cv2.FONT_HERSHEY_PLAIN, 1, (200, 200, 200))
cv2.imshow('Spectrogram', iSpec2)
cv2.moveWindow('Spectrogram', 50,
HeightPlot + statusHeight + 60)
# Show chromagram
if showChromagram:
iChroma = numpy.array(
(chromagram.T / chromagram.max()) * 255,
dtype=numpy.uint8)
iChroma2 = cv2.resize(iChroma, (WidthPlot, HeightPlot),
interpolation=cv2.INTER_CUBIC)
iChroma2 = cv2.applyColorMap(iChroma2, cv2.COLORMAP_JET)
cv2.putText(iChroma2, "maxFreqC: %s" % maxFreqC, (0, 11),
cv2.FONT_HERSHEY_PLAIN, 1, (200, 200, 200))
cv2.imshow('Chroma', iChroma2)
cv2.moveWindow('Chroma', 50,
2 * HeightPlot + statusHeight + 60)
# Activity Detection:
energy100 = (100 * numpy.sum(midTermBuffer * midTermBuffer) /
(midTermBuffer.shape[0] * 32000 * 32000))
if count < 10: # TODO make this param
energy100_buffer_zero.append(energy100)
mean_energy100_zero = numpy.mean(
numpy.array(energy100_buffer_zero))
else:
mean_energy100_zero = numpy.mean(
numpy.array(energy100_buffer_zero))
if (energy100 < 1.2 * mean_energy100_zero):
if curActiveWindow.shape[
0] > 0: # if a sound has been detected in the previous segment:
activeT2 = elapsedTime - BLOCKSIZE # set time of current active window
if activeT2 - activeT1 > minActivityDuration:
wavFileName = startDateTimeStr + "_activity_{0:.2f}_{1:.2f}.wav".format(
activeT1, activeT2)
if recordActivity:
wavfile.write(
wavFileName, Fs,
numpy.int16(curActiveWindow)
) # write current active window to file
curActiveWindow = numpy.array(
[]) # delete current active window
else:
if curActiveWindow.shape[
0] == 0: # this is a new active window!
activeT1 = elapsedTime - BLOCKSIZE # set timestamp start of new active window
curActiveWindow = numpy.concatenate(
(curActiveWindow, midTermBuffer))
# Show status messages on Status cv winow:
textIm = numpy.zeros((statusHeight, WidthPlot, 3))
statusStrTime = "time: %.1f sec" % elapsedTime + " - data time: %.1f sec" % dataTime + " - loss : %.1f sec" % (
elapsedTime - dataTime)
statusStrFeature = "ene1:%.1f" % energy100 + " eneZero:%.1f" % mean_energy100_zero
cv2.putText(textIm, statusStrTime, (0, 11),
cv2.FONT_HERSHEY_PLAIN, 1, (200, 200, 200))
cv2.putText(textIm, statusStrFeature, (0, 22),
cv2.FONT_HERSHEY_PLAIN, 1, (200, 200, 200))
if curActiveWindow.shape[0] > 0:
cv2.putText(textIm, "sound", (0, 33),
cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255))
else:
cv2.putText(textIm, "silence", (0, 33),
cv2.FONT_HERSHEY_PLAIN, 1, (200, 200, 220))
cv2.imshow("Status", textIm)
cv2.moveWindow("Status", 50, 0)
midTermBuffer = []
ch = cv2.waitKey(10)
count += 1
except IOError, e:
print("(%d) Error recording: %s" % (errorcount, e))
def recordAndCalcHR(BLOCKSIZE,
Fs,
showSpectrogram=False,
showChromagram=False,
recordActivity=False):
print("Press Ctr+C to stop process")
startDateTimeStr = datetime.datetime.now().strftime("%Y_%m_%d_%I:%M%p")
MEAN, STD = loadMEANS("svmMovies8classesMEANS") # load MEAN feature values
inp = alsaaudio.PCM(alsaaudio.PCM_CAPTURE,
alsaaudio.PCM_NONBLOCK) # open alsaaudio capture
inp.setchannels(1) # 1 channel
inp.setrate(Fs) # set sampling freq
inp.setformat(alsaaudio.PCM_FORMAT_S16_LE) # set 2-byte sample
inp.setperiodsize(512)
midTermBufferSize = int(Fs * BLOCKSIZE)
midTermBuffer = []
curWindow = []
count = 0
global allData
allData = []
energy100_buffer_zero = []
curActiveWindow = numpy.array([])
timeStart = time.time()
while 1:
l, data = inp.read() # read data from buffer
if l:
for i in range(len(data) / 2):
curWindow.append(audioop.getsample(data, 2,
i)) # get audio samples
if (len(curWindow) + len(midTermBuffer) > midTermBufferSize):
samplesToCopyToMidBuffer = midTermBufferSize - len(
midTermBuffer)
else:
samplesToCopyToMidBuffer = len(curWindow)
midTermBuffer = midTermBuffer + curWindow[
0:samplesToCopyToMidBuffer]
# copy to midTermBuffer
del (curWindow[0:samplesToCopyToMidBuffer])
if len(midTermBuffer
) == midTermBufferSize: # if midTermBuffer is full:
elapsedTime = (time.time() - timeStart
) # time since recording started
dataTime = (count + 1) * BLOCKSIZE # data-driven time
# TODO
# mtF, _ = aF.mtFeatureExtraction(midTermBuffer, Fs, BLOCKSIZE * Fs, BLOCKSIZE * Fs, 0.050 * Fs, 0.050 * Fs)
# curFV = (mtF - MEAN) / STD
# TODO
allData += midTermBuffer
midTermBuffer = numpy.double(
midTermBuffer) # convert current buffer to numpy array
# Compute spectrogram
if showSpectrogram:
(spectrogram, TimeAxisS, FreqAxisS) = aF.stSpectogram(
midTermBuffer, Fs, 0.020 * Fs,
0.02 * Fs) # extract spectrogram
FreqAxisS = numpy.array(FreqAxisS) # frequency axis
DominantFreqs = FreqAxisS[numpy.argmax(
spectrogram, axis=1
)] # most dominant frequencies (for each short-term window)
maxFreq = numpy.mean(
DominantFreqs) # get average most dominant freq
maxFreqStd = numpy.std(DominantFreqs)
# Compute chromagram
if showChromagram:
(chromagram, TimeAxisC,
FreqAxisC) = aF.stChromagram(midTermBuffer, Fs,
0.020 * Fs,
0.02 * Fs) # get chromagram
FreqAxisC = numpy.array(
FreqAxisC) # frequency axis (12 chroma classes)
DominantFreqsC = FreqAxisC[numpy.argmax(
chromagram, axis=1)] # most dominant chroma classes
maxFreqC = most_common(DominantFreqsC)[
0] # get most common among all short-term windows
# Plot signal window
signalPlotCV = plotCV(
scipy.signal.resample(midTermBuffer + 16000, WidthPlot),
WidthPlot, HeightPlot, 32000)
cv2.imshow('Signal', signalPlotCV)
cv2.moveWindow('Signal', 50, statusHeight + 50)
# Show spectrogram
if showSpectrogram:
iSpec = numpy.array(spectrogram.T * 255, dtype=numpy.uint8)
iSpec2 = cv2.resize(iSpec, (WidthPlot, HeightPlot),
interpolation=cv2.INTER_CUBIC)
iSpec2 = cv2.applyColorMap(iSpec2, cv2.COLORMAP_JET)
cv2.putText(iSpec2, "maxFreq: %.0f Hz" % maxFreq, (0, 11),
cv2.FONT_HERSHEY_PLAIN, 1, (200, 200, 200))
cv2.imshow('Spectrogram', iSpec2)
cv2.moveWindow('Spectrogram', 50,
HeightPlot + statusHeight + 60)
# Show chromagram
if showChromagram:
iChroma = numpy.array(
(chromagram.T / chromagram.max()) * 255,
dtype=numpy.uint8)
iChroma2 = cv2.resize(iChroma, (WidthPlot, HeightPlot),
interpolation=cv2.INTER_CUBIC)
iChroma2 = cv2.applyColorMap(iChroma2, cv2.COLORMAP_JET)
cv2.putText(iChroma2, "maxFreqC: %s" % maxFreqC, (0, 11),
cv2.FONT_HERSHEY_PLAIN, 1, (200, 200, 200))
cv2.imshow('Chroma', iChroma2)
cv2.moveWindow('Chroma', 50,
2 * HeightPlot + statusHeight + 60)
# Activity Detection:
energy100 = (100 * numpy.sum(midTermBuffer * midTermBuffer) /
(midTermBuffer.shape[0] * 32000 * 32000))
if count < 10: # TODO make this param
energy100_buffer_zero.append(energy100)
mean_energy100_zero = numpy.mean(
numpy.array(energy100_buffer_zero))
else:
mean_energy100_zero = numpy.mean(
numpy.array(energy100_buffer_zero))
if (energy100 < 1.2 * mean_energy100_zero):
if curActiveWindow.shape[
0] > 0: # if a sound has been detected in the previous segment:
activeT2 = elapsedTime - BLOCKSIZE # set time of current active window
if activeT2 - activeT1 > minActivityDuration:
wavFileName = startDateTimeStr + "_activity_{0:.2f}_{1:.2f}.wav".format(
activeT1, activeT2)
if recordActivity:
wavfile.write(
wavFileName, Fs,
numpy.int16(curActiveWindow)
) # write current active window to file
curActiveWindow = numpy.array(
[]) # delete current active window
else:
if curActiveWindow.shape[
0] == 0: # this is a new active window!
activeT1 = elapsedTime - BLOCKSIZE # set timestamp start of new active window
curActiveWindow = numpy.concatenate(
(curActiveWindow, midTermBuffer))
# Show status messages on Status cv winow:
textIm = numpy.zeros((statusHeight, WidthPlot, 3))
statusStrTime = "time: %.1f sec" % elapsedTime + " - data time: %.1f sec" % dataTime + " - loss : %.1f sec" % (
elapsedTime - dataTime)
statusStrFeature = "ene1:%.1f" % energy100 + " eneZero:%.1f" % mean_energy100_zero
cv2.putText(textIm, statusStrTime, (0, 11),
cv2.FONT_HERSHEY_PLAIN, 1, (200, 200, 200))
cv2.putText(textIm, statusStrFeature, (0, 22),
cv2.FONT_HERSHEY_PLAIN, 1, (200, 200, 200))
if curActiveWindow.shape[0] > 0:
cv2.putText(textIm, "sound", (0, 33),
cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255))
else:
cv2.putText(textIm, "silence", (0, 33),
cv2.FONT_HERSHEY_PLAIN, 1, (200, 200, 220))
cv2.imshow("Status", textIm)
cv2.moveWindow("Status", 50, 0)
midTermBuffer = []
ch = cv2.waitKey(10)
count += 1
"""!
@brief Example 04
@details pyAudioAnalysis spectrogram calculation and visualization example
@author Theodoros Giannakopoulos {[email protected]}
"""
import numpy as np
import scipy.io.wavfile as wavfile
import plotly
import plotly.graph_objs as go
from pyAudioAnalysis import audioFeatureExtraction as aF
layout = go.Layout(
title='Spectrogram Extraction Example using pyAudioAnalysis',
xaxis=dict(title='time (sec)', ),
yaxis=dict(title='Freqs (Hz)', ))
def normalize_signal(signal):
signal = np.double(signal)
signal = signal / (2.0**15)
return (signal - signal.mean()) / ((np.abs(signal)).max() + 0.0000000001)
if __name__ == '__main__':
[Fs, s] = wavfile.read("../data/sample_music.wav")
s = normalize_signal(s)
[S, t, f] = aF.stSpectogram(s, Fs, int(Fs * 0.020), int(Fs * 0.020))
heatmap = go.Heatmap(z=S.T, y=f, x=t)
plotly.offline.plot(go.Figure(data=[heatmap], layout=layout),
filename="temp.html",
auto_open=True)
def main(argv):
dirName = argv[1]
types = ('*.wav', )
filesList = []
for files in types:
filesList.extend(glob.glob(os.path.join(dirName, files)))
filesList = sorted(filesList)
WIDTH_SEC = 2.4
stWin = 0.020
stStep = 0.015
WIDTH = WIDTH_SEC / stStep
for f in filesList:
[Fs, x] = audioBasicIO.readAudioFile(f)
print(Fs)
x = audioBasicIO.stereo2mono(x)
specgramOr, TimeAxis, FreqAxis = aF.stSpectogram(
x, Fs, round(Fs * stWin), round(Fs * stStep), False)
if specgramOr.shape[0] > WIDTH:
specgram = specgramOr[int(specgramOr.shape[0] / 2) -
WIDTH / 2:int(specgramOr.shape[0] / 2) +
WIDTH / 2, :]
specgram = scipy.misc.imresize(specgram,
float(227.0) /
float(specgram.shape[0]),
interp='bilinear')
print specgram.shape
im = Image.fromarray(numpy.uint8(
matplotlib.cm.jet(specgram) * 255))
#plt.imshow(im)
scipy.misc.imsave(f.replace(".wav", ".jpg"), im)
if int(specgramOr.shape[0] / 2) - WIDTH / 2 - int(
(0.2) / stStep) > 0:
specgram = specgramOr[
int(specgramOr.shape[0] / 2) - WIDTH / 2 -
int((0.2) / stStep):int(specgramOr.shape[0] / 2) +
WIDTH / 2 - int((0.2) / stStep), :]
specgram = scipy.misc.imresize(specgram,
float(227.0) /
float(specgram.shape[0]),
interp='bilinear')
im = Image.fromarray(
numpy.uint8(matplotlib.cm.jet(specgram) * 255))
print specgram.shape
scipy.misc.imsave(f.replace(".wav", "_02A.jpg"), im)
specgram = specgramOr[
int(specgramOr.shape[0] / 2) - WIDTH / 2 +
int((0.2) / stStep):int(specgramOr.shape[0] / 2) +
WIDTH / 2 + int((0.2) / stStep), :]
specgram = scipy.misc.imresize(specgram,
float(227.0) /
float(specgram.shape[0]),
interp='bilinear')
print specgram.shape
im = Image.fromarray(
numpy.uint8(matplotlib.cm.jet(specgram) * 255))
scipy.misc.imsave(f.replace(".wav", "_02B.jpg"), im)
# ONLY FOR SPEECH (fewer samples). Must comment for music
"""specgram = specgramOr[int(specgramOr.shape[0]/2) - WIDTH/2 - int((0.1) / stStep):int(specgramOr.shape[0]/2) + WIDTH/2 - int((0.1) / stStep), :]
from pyAudioAnalysis import audioTrainTest as aT
from pyAudioAnalysis import audioSegmentation as aS
import matplotlib.pyplot as plt
root_data_path = "/Users/tyiannak/ResearchData/Audio Dataset/pyAudioAnalysisData/"
print("\n\n\n * * * TEST 1 * * * \n\n\n")
[Fs, x] = audioBasicIO.readAudioFile(root_data_path + "pyAudioAnalysis/data/count.wav");
F, f_names = audioFeatureExtraction.stFeatureExtraction(x, Fs, 0.050*Fs, 0.025*Fs);
plt.subplot(2,1,1); plt.plot(F[0,:]); plt.xlabel('Frame no'); plt.ylabel(f_names[0]);
plt.subplot(2,1,2); plt.plot(F[1,:]); plt.xlabel('Frame no'); plt.ylabel(f_names[1]); plt.show()
print("\n\n\n * * * TEST 2 * * * \n\n\n")
[Fs, x] = audioBasicIO.readAudioFile(root_data_path + "pyAudioAnalysis/data/doremi.wav")
x = audioBasicIO.stereo2mono(x)
specgram, TimeAxis, FreqAxis = audioFeatureExtraction.stSpectogram(x, Fs, round(Fs * 0.040), round(Fs * 0.040), True)
print("\n\n\n * * * TEST 3 * * * \n\n\n")
[Fs, x] = audioBasicIO.readAudioFile(root_data_path + "pyAudioAnalysis/data/doremi.wav")
x = audioBasicIO.stereo2mono(x)
specgram, TimeAxis, FreqAxis = audioFeatureExtraction.stChromagram(x, Fs, round(Fs * 0.040), round(Fs * 0.040), True)
print("\n\n\n * * * TEST 4 * * * \n\n\n")
aT.featureAndTrain([root_data_path +"SM/speech",root_data_path + "SM/music"], 1.0, 1.0, 0.2, 0.2, "svm", "temp", True)
print("\n\n\n * * * TEST 5 * * * \n\n\n")
[flagsInd, classesAll, acc, CM] = aS.mtFileClassification(root_data_path + "pyAudioAnalysis/data//scottish.wav", root_data_path + "pyAudioAnalysis/data/svmSM", "svm", True, root_data_path + 'pyAudioAnalysis/data/scottish.segments')
print("\n\n\n * * * TEST 6 * * * \n\n\n")
aS.trainHMM_fromFile(root_data_path + 'radioFinal/train/bbc4A.wav', root_data_path + 'radioFinal/train/bbc4A.segments', 'hmmTemp1', 1.0, 1.0)
aS.trainHMM_fromDir(root_data_path + 'radioFinal/small', 'hmmTemp2', 1.0, 1.0)
