def run(self):
global datastream
global detected_chord
block = True # The first read from the queue is blocking ...
data = q.get(block=block)
shift = len(data)
datastream = np.roll(datastream, -shift, axis=0)
datastream[-shift:, :] = data
#datastream = data
# framing audio
nfft = 8192
hop_size = 4096
nFrames = int(np.round(len(datastream) / (nfft - hop_size)))
x = np.append(datastream, np.zeros(nfft))
xFrame = np.empty((nfft, nFrames))
start = 0
chroma = np.empty((12, nFrames))
timestamp = np.zeros(nFrames)
# compute PCP
for n in range(nFrames):
xFrame[:, n] = x[start:start + nfft]
start = start + nfft - hop_size
chroma[:, n] = compute_chroma(xFrame[:, n], int(args.samplerate/args.downsample))
if np.all(chroma[:, n] == 0):
chroma[:, n] = np.finfo(float).eps
else:
chroma[:, n] /= np.max(np.absolute(chroma[:, n]))
timestamp[n] = n * (nfft - hop_size) / int(args.samplerate/args.downsample)
# get max probability path from Viterbi algorithm
(PI, A, B) = initialize(chroma, templates, nested_cof)
(path, states) = viterbi(PI, A, B)
# normalize path
for i in range(nFrames):
path[:, i] /= sum(path[:, i])
# choose most likely chord - with max value in 'path'
final_chords = []
indices = np.argmax(path, axis=0)
final_states = np.zeros(nFrames)
# find no chord zone
set_zero = np.where(np.max(path, axis=0) < 0.3 * np.max(path))[0]
if np.size(set_zero) is not 0:
indices[set_zero] = -1
# identify chords
for i in range(nFrames):
if indices[i] == -1:
final_chords.append('NC')
else:
final_states[i] = states[indices[i], i]
final_chords.append(chords[int(final_states[i])])
detected_chord = Counter(final_chords).most_common()
detected_chord = detected_chord[0][0]
print('Chord: ' + str(detected_chord))
def compute_chord_for_frame(frame, frame_rate):
frame = frame[::4]
frame_rate = int(frame_rate / 4)
chroma = compute_chroma(frame, frame_rate)
if np.all(chroma == 0):
chroma = np.finfo(float).eps
else:
chroma /= np.max(np.absolute(chroma))
#get max probability path from Viterbi algorithm
(PI,A,B) = initialize(chroma, templates, nested_cof)
(path, states) = viterbi(PI,A,B)
#normalize path
# for i in range(nFrames):
path /= sum(path)
#choose most likely chord - with max value in 'path'
indicator = np.argmax(path,axis=0)
#find no chord zone
set_zero = np.where(np.max(path,axis=0) < 0.3*np.max(path))
if np.size(set_zero) is not 0:
return 'NC'
#identify chords
final_state = states[indicator]
final_chord = chords[int(final_state)]
return final_chord
def get_chords(fs,s,chord):
x = s[::4]
x = x[:,1]
fs = int(fs/4)
#framing audio, window length = 8192, hop size = 1024 and computing PCP
nfft = 8192
hop_size = 1024
nFrames = int(np.round(len(x)/(nfft-hop_size)))
#zero padding to make signal length long enough to have nFrames
x = np.append(x, np.zeros(nfft))
xFrame = np.empty((nfft, nFrames))
start = 0
chroma = np.empty((12,nFrames))
id_chord = np.zeros(nFrames, dtype='int32')
timestamp = np.zeros(nFrames)
max_cor = np.zeros(nFrames)
#print(f'Chord: {chord}')
#print('Time (s)', 'Chord')
for n in range(nFrames):
xFrame[:,n] = x[start:start+nfft]
start = start + nfft - hop_size
timestamp[n] = n*(nfft-hop_size)/fs
chroma[:,n] = compute_chroma(xFrame[:,n],fs)
plt.figure(1)
plt.plot(chroma[:,n])
"""Correlate 12D chroma vector with each of 24 major and minor chords"""
cor_vec = np.zeros(24)
for ni in range(24):
cor_vec[ni] = np.correlate(chroma[:,n], np.array(templates[ni]))
max_cor[n] = np.max(cor_vec)
id_chord[n] = np.argmax(cor_vec) + 1
#if max_cor[n] < threshold, then no chord is played
#might need to change threshold value
id_chord[np.where(max_cor < 0.7*np.max(max_cor))] = 0
ident_chords = []
for n in range(nFrames):
#print(timestamp[n],chords[id_chord[n]])
ident_chords.append(chords[id_chord[n]])
return ident_chords
def frameByFrame(self, jName='chord_templates.json'):
tempJSON = readJSONChords(jName)
templates = createChordTemplate(tempJSON)
start = 0
chroma = np.empty((12, self.nFrames))
xFrame = np.empty((self.nfft, self.nFrames))
max_cor = np.zeros(self.nFrames)
print('Time(s)', 'Chord')
for n in range(self.nFrames):
xFrame[:, n] = self.MONO[start:start + self.nfft]
start = start + self.nfft - self.hopSize
self.timestamp[n] = n * (self.nfft - self.hopSize) / self.fs
chroma[:, n] = compute_chroma(xFrame[:, n], self.fs)
self.TIME.append(chroma[:, n])
"""Correlate 12D chroma vector with each of 24 major and minor chords"""
# +1 7th
cor_vec = np.zeros(24)
for ni in range(24):
cor_vec[ni] = np.correlate(chroma[:, n], np.array(templates[ni]))
# print("CHORD", chords[ni + 1], " Cor:", cor_vec[ni])
# print("CHROMA")
# print(chroma[:, n])
# print(" ")
max_cor[n] = np.max(cor_vec)
iMax = np.argmax(cor_vec) + 1
self.id_chord[n] = iMax
# print(max_cor[n], " ", chords[id_chord[n]])
# if max_cor[n] < threshold, then no chord is played
# might need to change threshold value
# id_chord[np.where(max_cor[n] < 0.80 * np.max(max_cor))] = 0
for n in range(self.nFrames):
if max_cor[n] > 0.80 * np.max(max_cor):
self.id_chord[n] = 0 #This maps to N == NO CHORD
#else:
# ++self.id_chord[n]
for n in range(self.nFrames):
print(self.timestamp[n], chords[self.id_chord[n]])
#zero padding to make signal length long enough to have nFrames
x = np.append(x, np.zeros(nfft))
xFrame = np.empty((nfft, nFrames))
start = 0
chroma = np.empty((12, nFrames))
id_chord = np.zeros(nFrames, dtype='int32')
timestamp = np.zeros(nFrames)
max_cor = np.zeros(nFrames)
print('Time (s)', 'Chord')
for n in range(nFrames):
xFrame[:, n] = x[start:start + nfft]
start = start + nfft - hop_size
timestamp[n] = n * (nfft - hop_size) / fs
chroma[:, n] = compute_chroma(xFrame[:, n], fs)
"""Correlate 12D chroma vector with each of 24 major and minor chords"""
cor_vec = np.zeros(24)
for ni in range(24):
cor_vec[ni] = np.correlate(chroma[:, n], np.array(templates[ni]))
max_cor[n] = np.max(cor_vec)
id_chord[n] = np.argmax(cor_vec) + 1
plt.specgram(x, Fs=fs)
#if max_cor[n] < threshold, then no chord is played
#might need to change threshold value
id_chord[np.where(max_cor < 0.8 * np.max(max_cor))] = 0
for n in range(nFrames):
print(timestamp[n], chords[id_chord[n]])
def get_chord_progress(path, win_sec, shift_sec):
with open('chord_templates.json', 'r') as fp:
templates_json = json.load(fp)
for chord in chords:
templates.append(templates_json[chord])
"""read audio and compute chromagram"""
(fs, s) = read(path)
#reduce sample rate and convert to mono
x = s[::4]
x = x[:, 1]
fs = int(fs / 4)
#framing audio
# nfft = 8192
# hop_size = 1024
nfft = int(win_sec * fs)
hop_size = int(shift_sec * fs)
nFrames = int(np.round(len(x) / (nfft - hop_size)))
#zero padding to make signal length long enough to have nFrames
x = np.append(x, np.zeros(nfft))
xFrame = np.empty((nfft, nFrames))
start = 0
chroma = np.empty((12, nFrames))
timestamp = np.zeros(nFrames)
#compute PCP
for n in range(nFrames):
xFrame[:, n] = x[start:start + nfft]
start = start + nfft - hop_size
chroma[:, n] = compute_chroma(xFrame[:, n], fs)
if np.all(chroma[:, n] == 0):
chroma[:, n] = np.finfo(float).eps
else:
chroma[:, n] /= np.max(np.absolute(chroma[:, n]))
timestamp[n] = n * (nfft - hop_size) / fs
#get max probability path from Viterbi algorithm
(PI, A, B) = initialize(chroma, templates, nested_cof)
(path, states) = viterbi(PI, A, B)
#normalize path
for i in range(nFrames):
path[:, i] /= sum(path[:, i])
#choose most likely chord - with max value in 'path'
final_chords = []
indices = np.argmax(path, axis=0)
final_states = np.zeros(nFrames)
#find no chord zone
set_zero = np.where(np.max(path, axis=0) < 0.3 * np.max(path))[0]
if np.size(set_zero) is not 0:
indices[set_zero] = -1
#identify chords
for i in range(nFrames):
if indices[i] == -1:
final_chords.append('NC')
else:
final_states[i] = states[indices[i], i]
final_chords.append(chords[int(final_states[i])])
# print('Time(s)','Chords')
# for i in range(nFrames):
# print(timestamp[i], final_chords[i])
return final_chords[1:-1]
# if __name__ == '__main__':
# get_chord_progress('./score/Disney_Themes_-_Fantasmic.wav',0.5, 0.25)
nFrames = int(np.round(len(x)/(nfft-hop_size))) #zero padding to make signal length long enough to have nFrames x = np.append(x, np.zeros(nfft)) xFrame = np.empty((nfft, nFrames)) start = 0 chroma = np.empty((12,nFrames)) id_chord = np.zeros(nFrames, dtype='int32') timestamp = np.zeros(nFrames) max_cor = np.zeros(nFrames) print 'Time (s)', 'Chord' for n in range(nFrames): xFrame[:,n] = x[start:start+nfft] start = start + nfft - hop_size timestamp[n] = n*(nfft-hop_size)/fs chroma[:,n] = compute_chroma(xFrame[:,n],fs) plt.figure(1) plt.plot(chroma[:,n]) """Correlate 12D chroma vector with each of 24 major and minor chords""" cor_vec = np.zeros(24) for ni in range(24): cor_vec[ni] = np.correlate(chroma[:,n], np.array(templates[ni])) max_cor[n] = np.max(cor_vec) id_chord[n] = np.argmax(cor_vec) + 1 #if max_cor[n] < threshold, then no chord is played #might need to change threshold value id_chord[np.where(max_cor < 0.8*np.max(max_cor))] = 0 for n in range(nFrames):
