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()
#plt.ion()
rawDataPath = os.path.join("..","rawData")
# extract all filenames
files = glob.glob(os.path.join(rawDataPath,"*.wav"))
names = []
for name in files:
fileName = os.path.basename(name).split(".")[0]
names.append(fileName)
# specify a specific file
filt = (None,None,['17']) #
audioFiles = pp.getKeys(names,filt);
# opens files and writes to a dictionary
(names,cDataset) = pp.readWAV(rawDataPath,audioFiles);
# cDataset = loadSubset(data,audioFiles);
def edges2fractures(ts, Fs=48000, edge_type='Sobel', smoothing=None):
"""
edges2fractures predicts peaks locations based on edges
in the signal spectrogram
Inputs
------
Fs: scalar
the frequency of the signal
edge_type: string
if __name__ == "__main__":
# importing common packages
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
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
Nf = 24000
# Nyquist freqency in Hz
Fpass = [3200 / Nf, 3300 / Nf]
Fstop = [3100 / Nf, 3400 / Nf]
N, Wn = scipy.signal.ellipord(Fpass, Fstop, 1, 60, False)
b, a = scipy.signal.ellip(N, 1, 60, Wn, 'bandpass')
w, h = scipy.signal.freqs(b, a, np.logspace(-4, 4, 500))
#plt.figure(figsize=(50,20))
#plt.semilogx(w, 20 * np.log10(abs(h)))
#plt.title('Elliptical bandpass filter isolate warning siren')
#plt.xlabel('Frequency [Hz]')
if __name__ == "__main__":
# importing common packages
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
