github/the-BPM-detector-python
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Fixing up for Python 3.6+

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This commit is contained in:
Robert Wlodarczyk
2019-12-07 20:31:37 -08:00
parent 61544a523e
commit 4156ea7ba0
1 changed files with 33 additions and 31 deletions
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64
bpm_detection/bpm_detection.py
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@@ -28,8 +28,8 @@ def read_wav(filename):
#open file, get metadata for audio
try:
wf = wave.open(filename,'rb')
except IOError, e:
print e
except IOError as e:
print(e)
return
# typ = choose_type( wf.getsampwidth() ) #TODO: implement choose_type
@@ -41,17 +41,17 @@ def read_wav(filename):
# read entire file and make into an array
samps = list(array.array('i',wf.readframes(nsamps)))
#print 'Read', nsamps,'samples from', filename
#print('Read', nsamps,'samples from', filename)
try:
assert(nsamps == len(samps))
except AssertionError, e:
print nsamps, "not equal to", len(samps)
except AssertionError as e:
print(nsamps, "not equal to", len(samps))
return samps, fs
# print an error when no data can be found
def no_audio_data():
print "No audio data for sample, skipping..."
print("No audio data for sample, skipping...")
return None, None
# simple peak detection
@@ -68,53 +68,55 @@ def bpm_detector(data,fs):
correl = []
cD_sum = []
levels = 4
max_decimation = 2**(levels-1);
min_ndx = 60./ 220 * (fs/max_decimation)
max_ndx = 60./ 40 * (fs/max_decimation)
max_decimation = 2**(levels-1)
min_ndx = math.floor(60./ 220 * (fs/max_decimation))
max_ndx = math.floor(60./ 40 * (fs/max_decimation))
for loop in range(0,levels):
cD = []
# 1) DWT
if loop == 0:
[cA,cD] = pywt.dwt(data,'db4');
cD_minlen = len(cD)/max_decimation+1;
cD_sum = numpy.zeros(cD_minlen);
[cA,cD] = pywt.dwt(data,'db4')
cD_minlen = len(cD)/max_decimation+1
cD_sum = numpy.zeros(math.floor(cD_minlen))
else:
[cA,cD] = pywt.dwt(cA,'db4');
[cA,cD] = pywt.dwt(cA,'db4')
# 2) Filter
cD = signal.lfilter([0.01],[1 -0.99],cD);
cD = signal.lfilter([0.01],[1 -0.99],cD)
# 4) Subtractargs.filename out the mean.
# 5) Decimate for reconstruction later.
cD = abs(cD[::(2**(levels-loop-1))]);
cD = cD - numpy.mean(cD);
cD = abs(cD[::(2**(levels-loop-1))])
cD = cD - numpy.mean(cD)
# 6) Recombine the signal before ACF
# essentially, each level I concatenate
# the detail coefs (i.e. the HPF values)
# to the beginning of the array
cD_sum = cD[0:cD_minlen] + cD_sum;
cD_sum = cD[0:math.floor(cD_minlen)] + cD_sum
if [b for b in cA if b != 0.0] == []:
return no_audio_data()
# adding in the approximate data as well...
cA = signal.lfilter([0.01],[1 -0.99],cA);
cA = abs(cA);
cA = cA - numpy.mean(cA);
cD_sum = cA[0:cD_minlen] + cD_sum;
cA = signal.lfilter([0.01],[1 -0.99],cA)
cA = abs(cA)
cA = cA - numpy.mean(cA)
cD_sum = cA[0:math.floor(cD_minlen)] + cD_sum
# ACF
correl = numpy.correlate(cD_sum,cD_sum,'full')
midpoint = len(correl) / 2
midpoint = math.floor(len(correl) / 2)
correl_midpoint_tmp = correl[midpoint:]
peak_ndx = peak_detect(correl_midpoint_tmp[min_ndx:max_ndx]);
peak_ndx = peak_detect(correl_midpoint_tmp[min_ndx:max_ndx])
if len(peak_ndx) > 1:
return no_audio_data()
peak_ndx_adjusted = peak_ndx[0]+min_ndx;
peak_ndx_adjusted = peak_ndx[0]+min_ndx
bpm = 60./ peak_ndx_adjusted * (fs/max_decimation)
print bpm
print(bpm)
return bpm,correl
@@ -134,15 +136,15 @@ if __name__ == '__main__':
n=0;
nsamps = len(samps)
window_samps = int(args.window*fs)
samps_ndx = 0; #first sample in window_ndx
max_window_ndx = nsamps / window_samps;
samps_ndx = 0 #first sample in window_ndx
max_window_ndx = math.floor(nsamps / window_samps)
bpms = numpy.zeros(max_window_ndx)
#iterate through all windows
for window_ndx in xrange(0,max_window_ndx):
for window_ndx in range(0,max_window_ndx):
#get a new set of samples
#print n,":",len(bpms),":",max_window_ndx,":",fs,":",nsamps,":",samps_ndx
#print(n,":",len(bpms),":",max_window_ndx_int,":",fs,":",nsamps,":",samps_ndx)
data = samps[samps_ndx:samps_ndx+window_samps]
if not ((len(data) % window_samps) == 0):
raise AssertionError( str(len(data) ) )
@@ -158,10 +160,10 @@ if __name__ == '__main__':
n=n+1; #counter for debug...
bpm = numpy.median(bpms)
print 'Completed. Estimated Beats Per Minute:', bpm
print('Completed. Estimated Beats Per Minute:', bpm)
n = range(0,len(correl))
plt.plot(n,abs(correl));
plt.show(False); #plot non-blocking
plt.show(block=False)
time.sleep(10);
plt.close();