def exeEachFeature(short_features,max_beat_time,plot):
hist_all = np.zeros((max_beat_time,))
selected_features=set_params.get_selected_features()
for i, element in enumerate(selected_features):
# dif threshold (3 x Mean of Difs)
dif_threshold = 2.0 * (np.abs(short_features[element, 0:-1] -
short_features[element, 1::])).mean()
if dif_threshold <= 0:
dif_threshold = 0.0000000000000001
# detect local maxima
[pos1, _] = utilities.peakdet(short_features[element, :], dif_threshold)
position_diffs = []
# compute histograms of local maxima changes
for j in range(len(pos1)-1):
position_diffs.append(pos1[j+1]-pos1[j])
histogram_times, histogram_edges = \
np.histogram(position_diffs, np.arange(0.5, max_beat_time + 1.5))
hist_centers = (histogram_edges[0:-1] + histogram_edges[1::]) / 2.0
histogram_times = \
histogram_times.astype(float) / short_features.shape[1]
hist_all += histogram_times
if plot:
plt.subplot(9, 2, i + 1)
plt.plot(short_features[element, :], 'k')
for k in pos1:
plt.plot(k, short_features[element, k], 'k*')
f1 = plt.gca()
f1.axes.get_xaxis().set_ticks([])
f1.axes.get_yaxis().set_ticks([])
return hist_centers
def beatExtraction(st_features, win_len, PLOT=False):
"""
This function extracts an estimate of the beat rate for a musical signal.
ARGUMENTS:
- st_features: a numpy array (n_feats x numOfShortTermWindows)
- win_len: window size in seconds
RETURNS:
- BPM: estimates of beats per minute
- Ratio: a confidence measure
"""
# Features that are related to the beat tracking task:
toWatch = [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]
max_beat_time = int(round(2.0 / win_len))
hist_all = numpy.zeros((max_beat_time,))
for ii, i in enumerate(toWatch): # for each feature
DifThres = 2.0 * (numpy.abs(st_features[i, 0:-1] - st_features[i, 1::])).mean() # dif threshold (3 x Mean of Difs)
if DifThres<=0:
DifThres = 0.0000000000000001
[pos1, _] = utilities.peakdet(st_features[i, :], DifThres) # detect local maxima
posDifs = [] # compute histograms of local maxima changes
for j in range(len(pos1)-1):
posDifs.append(pos1[j+1]-pos1[j])
[hist_times, HistEdges] = numpy.histogram(posDifs, numpy.arange(0.5, max_beat_time + 1.5))
hist_centers = (HistEdges[0:-1] + HistEdges[1::]) / 2.0
hist_times = hist_times.astype(float) / st_features.shape[1]
hist_all += hist_times
if PLOT:
plt.subplot(9, 2, ii + 1)
plt.plot(st_features[i, :], 'k')
for k in pos1:
plt.plot(k, st_features[i, k], 'k*')
f1 = plt.gca()
f1.axes.get_xaxis().set_ticks([])
f1.axes.get_yaxis().set_ticks([])
if PLOT:
plt.show(block=False)
plt.figure()
# Get beat as the argmax of the agregated histogram:
I = numpy.argmax(hist_all)
bpms = 60 / (hist_centers * win_len)
BPM = bpms[I]
# ... and the beat ratio:
Ratio = hist_all[I] / hist_all.sum()
if PLOT:
# filter out >500 beats from plotting:
hist_all = hist_all[bpms < 500]
bpms = bpms[bpms < 500]
plt.plot(bpms, hist_all, 'k')
plt.xlabel('Beats per minute')
plt.ylabel('Freq Count')
plt.show(block=True)
return BPM, Ratio
def beatExtraction(st_features, win_len, PLOT=False):
"""
This function extracts an estimate of the beat rate for a musical signal.
ARGUMENTS:
- st_features: a numpy array (n_feats x numOfShortTermWindows)
- win_len: window size in seconds
RETURNS:
- BPM: estimates of beats per minute
- Ratio: a confidence measure
"""
# Features that are related to the beat tracking task:
toWatch = [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]
max_beat_time = int(round(2.0 / win_len))
hist_all = numpy.zeros((max_beat_time,))
for ii, i in enumerate(toWatch): # for each feature
DifThres = 2.0 * (numpy.abs(st_features[i, 0:-1] - st_features[i, 1::])).mean() # dif threshold (3 x Mean of Difs)
if DifThres<=0:
DifThres = 0.0000000000000001
[pos1, _] = utilities.peakdet(st_features[i, :], DifThres) # detect local maxima
posDifs = [] # compute histograms of local maxima changes
for j in range(len(pos1)-1):
posDifs.append(pos1[j+1]-pos1[j])
[hist_times, HistEdges] = numpy.histogram(posDifs, numpy.arange(0.5, max_beat_time + 1.5))
hist_centers = (HistEdges[0:-1] + HistEdges[1::]) / 2.0
hist_times = hist_times.astype(float) / st_features.shape[1]
hist_all += hist_times
if PLOT:
plt.subplot(9, 2, ii + 1)
plt.plot(st_features[i, :], 'k')
for k in pos1:
plt.plot(k, st_features[i, k], 'k*')
f1 = plt.gca()
f1.axes.get_xaxis().set_ticks([])
f1.axes.get_yaxis().set_ticks([])
if PLOT:
plt.show(block=False)
plt.figure()
# Get beat as the argmax of the agregated histogram:
I = numpy.argmax(hist_all)
bpms = 60 / (hist_centers * win_len)
BPM = bpms[I]
# ... and the beat ratio:
Ratio = hist_all[I] / hist_all.sum()
if PLOT:
# filter out >500 beats from plotting:
hist_all = hist_all[bpms < 500]
bpms = bpms[bpms < 500]
plt.plot(bpms, hist_all, 'k')
plt.xlabel('Beats per minute')
plt.ylabel('Freq Count')
plt.show(block=True)
return BPM, Ratio
def beatExtraction(stFeatures, winSize, PLOT=False):
toWatch = [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]
maxBeatTime = int(round(2.0 / winSize))
HistAll = numpy.zeros((maxBeatTime,))
for ii, i in enumerate(toWatch): # for each feature
DifThres = 2.0 * (numpy.abs(stFeatures[i, 0:-1] - stFeatures[i, 1::])).mean() # dif threshold (3 x Mean of Difs)
[pos1, _] = utilities.peakdet(stFeatures[i, :], DifThres) # detect local maxima
posDifs = [] # compute histograms of local maxima changes
for j in range(len(pos1)-1):
posDifs.append(pos1[j+1]-pos1[j])
[HistTimes, HistEdges] = numpy.histogram(posDifs, numpy.arange(0.5, maxBeatTime + 1.5))
HistCenters = (HistEdges[0:-1] + HistEdges[1::]) / 2.0
HistTimes = HistTimes.astype(float) / stFeatures.shape[1]
HistAll += HistTimes
if PLOT:
plt.subplot(9, 2, ii + 1)
plt.plot(stFeatures[i, :], 'k')
for k in pos1:
plt.plot(k, stFeatures[i, k], 'k*')
f1 = plt.gca()
f1.axes.get_xaxis().set_ticks([])
f1.axes.get_yaxis().set_ticks([])
if PLOT:
plt.show(block=False)
plt.figure()
# Get beat as the argmax of the agregated histogram:
I = numpy.argmax(HistAll)
BPMs = 60 / (HistCenters * winSize)
BPM = BPMs[I]
# ... and the beat ratio:
Ratio = HistAll[I] / HistAll.sum()
if PLOT:
# filter out >500 beats from plotting:
HistAll = HistAll[BPMs < 500]
BPMs = BPMs[BPMs < 500]
plt.plot(BPMs, HistAll, 'k')
plt.xlabel('Beats per minute')
plt.ylabel('Freq Count')
plt.show(block=True)
return BPM, Ratio
def beat_extraction(short_features, window_size, plot=False):
"""
This function extracts an estimate of the beat rate for a musical signal.
ARGUMENTS:
- short_features: a np array (n_feats x numOfShortTermWindows)
- window_size: window size in seconds
RETURNS:
- bpm: estimates of beats per minute
- ratio: a confidence measure
"""
# Features that are related to the beat tracking task:
selected_features = [
0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18
]
max_beat_time = int(round(2.0 / window_size))
hist_all = np.zeros((max_beat_time, ))
# for each feature
for ii, i in enumerate(selected_features):
# dif threshold (3 x Mean of Difs)
dif_threshold = 2.0 * (np.abs(short_features[i, 0:-1] -
short_features[i, 1::])).mean()
if dif_threshold <= 0:
dif_threshold = 0.0000000000000001
# detect local maxima
[pos1, _] = utilities.peakdet(short_features[i, :], dif_threshold)
position_diffs = []
# compute histograms of local maxima changes
for j in range(len(pos1) - 1):
position_diffs.append(pos1[j + 1] - pos1[j])
histogram_times, histogram_edges = \
np.histogram(position_diffs, np.arange(0.5, max_beat_time + 1.5))
hist_centers = (histogram_edges[0:-1] + histogram_edges[1::]) / 2.0
histogram_times = \
histogram_times.astype(float) / short_features.shape[1]
hist_all += histogram_times
if plot:
plt.subplot(9, 2, ii + 1)
plt.plot(short_features[i, :], 'k')
for k in pos1:
plt.plot(k, short_features[i, k], 'k*')
f1 = plt.gca()
f1.axes.get_xaxis().set_ticks([])
f1.axes.get_yaxis().set_ticks([])
if plot:
plt.show(block=False)
plt.figure()
# Get beat as the argmax of the agregated histogram:
max_indices = np.argmax(hist_all)
bpms = 60 / (hist_centers * window_size)
bpm = bpms[max_indices]
# ... and the beat ratio:
ratio = hist_all[max_indices] / hist_all.sum()
if plot:
# filter out >500 beats from plotting:
hist_all = hist_all[bpms < 500]
bpms = bpms[bpms < 500]
plt.plot(bpms, hist_all, 'k')
plt.xlabel('Beats per minute')
plt.ylabel('Freq Count')
plt.show(block=True)
return bpm, ratio
