def listen(self):
p = pyaudio.PyAudio()
stream = p.open(format=pyaudio.paInt16,
channels=var.CHANNELS,
rate=var.RATE,
input=True,
frames_per_buffer=var.CHUNK)
print("* recording * ")
data = stream.read(var.CHUNK)
i = 0
last_match = -1
challenger = -1
successive_challenge = 0
while(True):
try:
data = stream.read(var.CHUNK)
except IOError:
pass
i += 1
if i%2 == 0 :
data_fft = da.normalize(da.getFft(data, var.RATE))
best_match, distance_to_best_match = self.match(data_fft)
if last_match == -1 :
last_match = best_match
print "I got " + best_match.name + " with an error of " + str(distance_to_best_match)
else :
if last_match != best_match :
# distance_to_last_match = da.distance(data_fft, last_match.chord_fft)
if best_match == challenger or challenger == -1 :
successive_challenge += 1
if successive_challenge > 3 :
print "I got " + best_match.name + " with an error of " + str(distance_to_best_match)
last_match = best_match
challenger = -1
successive_challenge = 0
else :
# print "o"
pass
else :
# print "x"
challenger = best_match
successive_challenge = 0
else :
# print "."
challenger = -1
successive_challenge = 0
print("* done recording")
stream.stop_stream()
stream.close()
p.terminate()
def learn_from_wave_file(self, file_path) :
print "Learning for "+ self.name
wf = wave.open(file_path, 'rb')
dictionaries = []
p = pyaudio.PyAudio()
stream = p.open(format=p.get_format_from_width(wf.getsampwidth()),
channels=wf.getnchannels(),
rate=wf.getframerate(),
output=True)
data = wf.readframes(var.CHUNK)
while data != '':
dictionaries.append(da.normalize(da.getFft(data, var.RATE)))
data = wf.readframes(var.CHUNK)
stream.stop_stream()
stream.close()
# Average the dictionary
mean_dictionnary = da.average_dictionary(dictionaries)
print "Done Learning"
threshold = 0 ;
for fft_data in dictionaries :
threshold += da.distance(fft_data, mean_dictionnary)
threshold /= len(dictionaries)
print "Done setting the threshold at " + str(threshold)
real_list = []
for fft_data in dictionaries :
if da.distance(fft_data, mean_dictionnary) < threshold :
real_list.append(fft_data)
new_mean = da.average_dictionary(real_list)
print "Done filtering"
print "Distance between the new and old means is " + str(da.distance(mean_dictionnary, new_mean))
threshold = 0 ;
for fft_data in real_list :
threshold += da.distance(fft_data, new_mean)
threshold /= len(real_list)
print "Done resetting the threshold at " + str(threshold)
print len(real_list)
self.chord_fft = new_mean
self.threshold = threshold
