def print_info(self, now, subsample=None, norm_correlation=None, delta=1):
if self.buf is None:
return
now = float(now)
buffer_waveform = self.get_arr()
print "({0}, {1})".format((now - len(buffer_waveform)) / self.rate, now / self.rate)
rtransform = abs(fft(buffer_waveform))[:len(buffer_waveform)/2]
peaks = find_peaks(rtransform, delta)
peaks.sort(key=lambda x: -x[1])
for peak in peaks:
peak = (peak[0] * self.rate / float(len(buffer_waveform)), peak[1])
print peak
def get_note(region, sample_rate): # Piano note. a4_pitch = 440 # A list of frequencies can be found here: # http://en.wikipedia.org/wiki/Piano_key_frequencies # A sample of FFTs per piano key can be found here: # https://www.youtube.com/watch?v=5xjD6SRY8Pg # The perceived note that we hear and associate with a certain frequency # are actually a series of harmonic peaks. The fundamental frequency # might be missing from the FFT. # There are three ways that I know of to get a note through the FFT: # Look at peaks: I don't think this is happening without prior knowledge # to what the note will be (which we will have...) # Maybe we could look into some sort of machine learning for this. # Autocorrelation: Never looked into this. # Harmonic product spectrum: I'm using this for now. # Basic idea -- http://cnx.org/content/m11714/latest/ # TBH I think this is the wrong approach, but I don't know what approach # to take given the notes we have from the MusicXML file. # Compress the FFT to 1/2, 1/3 & 1/4 to its size. # Could be made more efficient or something but whatever. max_harmonics = 4 original_freqs = fft(region, sample_rate) hps_result = copy(original_freqs) for i in xrange(2, max_harmonics + 1): compressed_freqs = copy(original_freqs[::i]) hps_result = hps_result[:len(compressed_freqs)] hps_result *= compressed_freqs # Find resulting peak here. return find_peaks(hps_result, 0.5)[0] # i dunno lol
