import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
import os
# importing local packages
import preprocessing as pp
# reading all the audio data into a dictionary
audioPath = os.path.join(os.getcwd(), "..", "..", "rawAudio")
filenames, audioDict = pp.readWAV(audioPath)
filenames, audioDict = pp.readWAV(audioPath)
# extracting the filenames of interest
keys = pp.getKeys(filenames, (["B1"], None, ["15"]))
# subsetting the data
subset = pp.loadSubset(audioDict, keys)
# Note: for now we design sync_envelope to work with two signals
# However, there may be several time processing steps which could be applied to all of the signals at the same time: instead of two by two.
# it is possible that even the cross-correlation can be spead up by doing it in ND
# add test using a specific sine wave or something
# add test for shifting the same signal
# we assume the signals are 1D
signal1 = subset.values()[0][:, 0]
signal2 = subset.values()[1][:, 0]
# TEST THE FUNCTIONS
import preprocessing as pp
rawDataPath = os.path.join("..","rawData"); # directory where the audio fles reside
files = glob.glob(os.path.join(rawDataPath,"*.wav"))
names = [];
for name in files:
fileName = os.path.basename(name).split(".")[0]
names.append(fileName)
filt = (None,None,['17']) #
audioFiles = pp.getKeys(names,filt);
(names,cDataset) = pp.readWAV(rawDataPath,audioFiles); # opens files and writes to a dictionary
#cDataset = loadSubset(data,audioFiles);
plt.figure(figsize=(50,20))
for ii in range(len(cDataset)):
plt.subplot(len(cDataset)/2,2,ii+1)
(Pxx,freqs,bins,im) = plt.specgram(cDataset[audioFiles[ii]][:,0],NFFT=2048,Fs=48000,noverlap=900,cmap=plt.cm.gist_heat)
#plt.plot(cDataset[audioFiles[ii]][:,0]-cDataset[audioFiles[ii]][:,1],'b')
#plt.ylim([3000,3800])
plt.draw()
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
import os
# importing local packages
import preprocessing as pp
# reading all the audio data into a dictionary
audioPath = os.path.join(os.getcwd(), '..', '..', 'rawAudio')
filenames, audioDict = pp.readWAV(audioPath)
filenames, audioDict = pp.readWAV(audioPath)
# extracting the filenames of interest
keys = pp.getKeys(filenames, (['B1'], None, ['15']))
# subsetting the data
subset = pp.loadSubset(audioDict, keys)
# Note: for now we design sync_envelope to work with two signals
# However, there may be several time processing steps which could be applied to all of the signals at the same time: instead of two by two.
# it is possible that even the cross-correlation can be spead up by doing it in ND
# add test using a specific sine wave or something
# add test for shifting the same signal
# we assume the signals are 1D
signal1 = subset.values()[0][:, 0]
signal2 = subset.values()[1][:, 0]