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]
Beispiel #2
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()
Beispiel #3
0
    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]