class Recognise():
def __init__(self):
self.filename = 'Samples/336739__astronautchild__goddog.wav'
self.f = Fingerprint(self.filename)
self.fprint = self.f.fingerprint()
self.window_size = 22050/16.0
logging.debug("Recognise initialised")
def compare(self, sample, fprint):
t_comp = []
for win_start in range(0, len(fprint)):
logging.info("Comparing {0} of {1}".format(win_start+1, len(fprint)))
win_comp = []
for idx, window in enumerate(sample):
try:
win_or = fprint[win_start + idx]
except:
win_or = None
win_comp.append(self.compare_window(window, win_or))
t_comp.append(win_comp)
plt.plot([sum(win) for win in t_comp])
plt.show()
def compare_window(self, window, fprint):
"""
Takes in two lists of frequencies
and compares them to produce a similarity
figure.
The figures it takes in should be frequencies
with the max amplitudes in segments of a
fourier transformed window
"""
if fprint is None:
return 0.5 * len(window)
res = 0
for x, y in map(None, window, fprint):
if x is None:
res += 0.5
else:
res += abs(x-y) / self.window_size
return res
def __init__(self):
self.filename = 'Samples/336739__astronautchild__goddog.wav'
self.f = Fingerprint(self.filename)
self.fprint = self.f.fingerprint()
self.window_size = 22050/16.0
logging.debug("Recognise initialised")
win_or = None
win_comp.append(self.compare_window(window, win_or))
t_comp.append(win_comp)
plt.plot([sum(win) for win in t_comp])
plt.show()
def compare_window(self, window, fprint):
"""
Takes in two lists of frequencies
and compares them to produce a similarity
figure.
The figures it takes in should be frequencies
with the max amplitudes in segments of a
fourier transformed window
"""
if fprint is None:
return 0.5 * len(window)
res = 0
for x, y in map(None, window, fprint):
if x is None:
res += 0.5
else:
res += abs(x-y) / self.window_size
return res
if __name__ == "__main__":
r = Recognise()
f = Fingerprint(r.filename)
sample = f.fingerprint()[40:80]
r.compare(sample, r.fprint)
