def getFreq(audio, rate):
#Filter with Hanning Window
window = hann(len(audio))
audio = audio * window
#run rfft
spectrum = rfft(audio)
#find peak of signal
#This segment is Copyright (c) 2007, Imri Goldberg
signature = [peak for peak in fft_utils.find_peaks(spectrum,2, 1e6) if peak[0]>10]
print signature, " signature\n"
if len(signature) == 0:
#0 indicates REST. Returning none is faulty.
return 0
note = min([x[0] for x in signature])
return float(note)*2*rate/(2**13)
def getFreq(audio, rate):
#Filter with Hanning Window
window = hann(len(audio))
audio = audio * window
#run rfft
spectrum = rfft(audio)
#find peak of signal
#This segment is Copyright (c) 2007, Imri Goldberg
signature = [
peak for peak in fft_utils.find_peaks(spectrum, 2, 1e6) if peak[0] > 10
]
print signature, " signature\n"
if len(signature) == 0:
#0 indicates REST. Returning none is faulty.
return 0
note = min([x[0] for x in signature])
return float(note) * 2 * rate / (2**13)
def recognize_note(self, audio_data):
"""return the note played in the given audio data
Params:
audio_data - a buffer of samples
Return value:
The frequency of the playing note.
If no playing note is found, None is returned.
"""
mult = fft_utils.awindow(len(audio_data))
final_fft = numpy.array(map(abs, numpy.fft.fft(audio_data*mult)[:self.fft_size/12]))
signature = [peak for peak in fft_utils.find_peaks(final_fft,2) if peak[0]>10]
if len(signature) == 0:
return
note = min([x[0] for x in signature])
return self.compute_freq(note)
def recognize_note(self, audio_data):
"""return the note played in the given audio data
Params:
audio_data - a buffer of samples
Return value:
The frequency of the playing note.
If no playing note is found, None is returned.
"""
mult = fft_utils.awindow(len(audio_data))
final_fft = numpy.array(
map(abs,
numpy.fft.fft(audio_data * mult)[:self.fft_size / 12]))
signature = [
peak for peak in fft_utils.find_peaks(final_fft, 2) if peak[0] > 10
]
if len(signature) == 0:
return
note = min([x[0] for x in signature])
return self.compute_freq(note)