def monophonicWavToMidi(wav_filename, midi_filename):
# extract wavfile information
[samplingRate, data] = scipy.io.wavfile.read(wav_filename)
data = np.sum(data,
1) # average to handle cases with more than one channel
onsets, durations, pitches = getSignalInformation(
msignal.Signal(samplingRate, data))
# construct a midi file from the above info
track = 0
channel = 0
volume = 127
tempo = 60 # 60 beats per minute or 1 beat per second
midi = midiutil.MidiFile.MIDIFile(adjust_origin=None)
midi.addTempo(track, 0, tempo)
for i in xrange(len(onsets)):
time = float(onsets[i]) / samplingRate
duration = float(durations[i]) / samplingRate
# Formula for MIDI pitch in the beow wiki page
# https://en.wikipedia.org/wiki/MIDI_Tuning_Standard
pitch = int(np.round(69 + 12 * np.log2(float(pitches[i]) / 440)))
midi.addNote(track, channel, pitch, time, duration, volume)
with open(midi_filename, 'wb') as output_file:
midi.writeFile(output_file)
detection, threshold = data[i], thresholdFunction[i]
if detection > threshold: # only accept those greater than the threshold
left, right = max(0, i - radius), min(len(data), i + radius + 1)
if detection == np.amax(
data[left:right]
): # make sure that we have the max in a radius
peakLocs.append(i)
return np.array(peakLocs)
if __name__ == '__main__':
kiki = 'dataset/Kiki-A-Town-with-an-Ocean-View.wav'
twinkle = 'dataset/twinkle twinkle little star.wav'
samplingRate, data = scipy.io.wavfile.read(twinkle)
data = np.add(data[:, 0], data[:, 1])
signal = msignal.Signal(samplingRate, data)
sd = spectral_difference.spectralDifference(signal,
default_params.WINDOW_SIZE,
default_params.HOP_SIZE)
sdUpsampled = scipy.signal.resample(sd, len(sd) * default_params.HOP_SIZE)
thresholds = filtering.medianFilter(
sd, default_params.MEDIAN_FILTER_KERNEL_SIZE,
default_params.MEDIAN_FILTER_DELTA,
default_params.MEDIAN_FILTER_LAMBDA)
peakLocs = findPeaks(sd, thresholds, default_params.PEAK_FINDING_RADIUS)
onsets = peakLocs * default_params.HOP_SIZE
plt.figure(1)
plt.subplot(211)
plt.plot(sdUpsampled)
plt.subplot(212)
plt.plot(data)
def getPitch(index):
return pitch_detection.getGeneralPitch(msignal.Signal(signal.samplingRate, \
signal.truncate(onsets[index], offsets[index]).data))
