26b997811a
p_ac.py : changed dutyCyclePct to a list, added real-timen note analysis and multi-pitch tests. Added keyboard.py, rt_note_analysis.py, plot_note_analysis.py, plot_all_note_durations.ipynb.
520 lines
16 KiB
Python
520 lines
16 KiB
Python
import os, sys
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import matplotlib.pyplot as plt
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import numpy as np
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from common import parse_yaml_cfg
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from rms_analysis import rms_analysis_main
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from rms_analysis import select_first_stable_note_by_delta_db
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from rms_analysis import select_first_stable_note_by_dur
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def is_nanV( xV ):
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for i in range(xV.shape[0]):
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if np.isnan( xV[i] ):
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return True
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return False
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def _find_max_take_id( inDir ):
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id = 0
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while os.path.isdir( os.path.join(inDir, "%i" % id) ):
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id += 1
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if id > 0:
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id -= 1
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return id
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def form_final_pulse_list( inDir, midi_pitch, analysisArgsD, take_id=None ):
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# append the midi pitch to the input directory
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#inDir = os.path.join( inDir, "%i" % (midi_pitch))
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dirL = os.listdir(inDir)
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pkL = []
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# for each take in this directory
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for idir in dirL:
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take_number = int(idir)
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# analyze this takes audio and locate the note peaks
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r = rms_analysis_main( os.path.join(inDir,idir), midi_pitch, **analysisArgsD['rmsAnalysisArgs'] )
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# store the peaks in pkL[ (db,us) ]
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for db,us in zip(r.pkDbL,r.pkUsL):
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pkL.append( (db,us) )
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# sort the peaks on increasing attack pulse microseconds
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pkL = sorted( pkL, key= lambda x: x[1] )
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# merge sample points that separated by less than 'minSampleDistUs' milliseconds
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pkL = merge_close_sample_points( pkL, analysisArgsD['minSampleDistUs'] )
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# split pkL
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pkDbL,pkUsL = tuple(zip(*pkL))
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#-------------------------------------------
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# locate the first and last note
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min_pk_idx, max_pk_idx = find_min_max_peak_index( pkDbL, analysisArgsD['minAttkDb'], analysisArgsD['maxDbOffset'] )
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db1 = pkDbL[ max_pk_idx ]
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db0 = pkDbL[ min_pk_idx ]
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pulseUsL = []
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pulseDbL = []
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multValL = []
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for out_idx in range(128):
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# calc the target volume
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db = db0 + (out_idx * (db1-db0)/127.0)
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multi_value_count = 0
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# look for the target between each of the sampled points
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for i in range(1,len(pkDbL)):
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# if the target volume is between these two sample points
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if pkDbL[i-1] <= db and db < pkDbL[i]:
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# if the target has not already been located
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if len(pulseUsL) == out_idx:
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# interpolate the pulse time from between the sampled points
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frac = (db - pkDbL[i-1]) / (pkDbL[i] - pkDbL[i-1])
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us = pkUsL[i-1] + frac * (pkUsL[i] - pkUsL[i-1])
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db = pkDbL[i-1] + frac * (pkDbL[i] - pkDbL[i-1])
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pulseUsL.append(us)
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pulseDbL.append(db)
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else:
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# this target db value was found between multiple sampled points
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# therefore the sampled volume function is not monotonic
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multi_value_count += 1
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if multi_value_count > 0:
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multValL.append((out_idx,multi_value_count))
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if len(multValL) > 0:
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# print("Multi-value pulse locations were found during velocity table formation: ",multValL)
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pass
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return pulseUsL,pulseDbL,r.holdDutyPctL
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def merge_close_sample_points( pkDbUsL, minSampleDistanceUs ):
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avg0Us = np.mean(np.diff([ x[1] for x in pkDbUsL ]))
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n0 = len(pkDbUsL)
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while True and n0>0:
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us0 = None
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db0 = None
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for i,(db,us) in enumerate(pkDbUsL):
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if i > 0 and us - us0 < minSampleDistanceUs:
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us1 = (us0 + us)/2
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db1 = (db0 + db)/2
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pkDbUsL[i-1] = (db1,us1)
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del pkDbUsL[i]
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break
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else:
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us0 = us
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db0 = db
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if i+1 == len(pkDbUsL):
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break
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avg1Us = np.mean(np.diff([ x[1] for x in pkDbUsL ]))
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print("%i sample points deleted by merging close points." % (n0 - len(pkDbUsL)))
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print("Mean time between samples - before:%f after:%f " % (avg0Us,avg1Us))
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print("Min time between samples: %i " % (np.min(np.diff([x[1] for x in pkDbUsL]))))
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return pkDbUsL
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def _calc_resample_points( dPkDb, pkUs0, pkUs1, samplePerDb, minSampleDistUs ):
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dPkUs = pkUs1 - pkUs0
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sampleCnt = max(int(round(abs(dPkDb) * samplePerDb)),samplePerDb)
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dUs = max(int(round(dPkUs/sampleCnt)),minSampleDistUs)
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sampleCnt = int(round(dPkUs/dUs))
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dUs = int(round(dPkUs/sampleCnt))
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usL = [ pkUs0 + dUs*j for j in range(sampleCnt+1)]
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return usL
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def calc_resample_ranges( pkDbL, pkUsL, min_pk_idx, max_pk_idx, maxDeltaDb, samplePerDb, minSampleDistUs ):
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if min_pk_idx == 0:
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print("No silent notes were generated. Decrease the minimum peak level or the hold voltage.")
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return None
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resampleUsSet = set()
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refPkDb = pkDbL[min_pk_idx]
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#pkDbL = pkDbL[ pkIdxL ]
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for i in range( min_pk_idx, max_pk_idx+1 ):
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d = pkDbL[i] - pkDbL[i-1]
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usL = []
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# if this peak is less than maxDeltaDb above the previous pk or
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# it is below the previous max peak
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if d > maxDeltaDb or d <= 0 or pkDbL[i] < refPkDb:
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usL = _calc_resample_points( d, pkUsL[i-1], pkUsL[i], samplePerDb, minSampleDistUs )
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if d <= 0 and i + 1 < len(pkDbL):
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d = pkDbL[i+1] - pkDbL[i]
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usL += _calc_resample_points( d, pkUsL[i-1], pkUsL[i], samplePerDb, minSampleDistUs )
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if pkDbL[i] > refPkDb:
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refPkDb = pkDbL[i]
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if usL:
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resampleUsSet = resampleUsSet.union( usL )
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return resampleUsSet
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def form_resample_pulse_time_list( inDir, analysisArgsD ):
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"""" This function merges all available data from previous takes to form
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a new list of pulse times to sample.
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"""
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# the last folder is always the midi pitch of the note under analysis
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midi_pitch = int( inDir.split("/")[-1] )
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dirL = os.listdir(inDir)
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pkL = []
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# for each take in this directory
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for idir in dirL:
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take_number = int(idir)
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# analyze this takes audio and locate the note peaks
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r = rms_analysis_main( os.path.join(inDir,idir), midi_pitch, **analysisArgsD['rmsAnalysisArgs'] )
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# store the peaks in pkL[ (db,us) ]
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for db,us in zip(r.pkDbL,r.pkUsL):
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pkL.append( (db,us) )
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# sort the peaks on increasing attack pulse microseconds
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pkL = sorted( pkL, key= lambda x: x[1] )
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# merge sample points that separated by less than 'minSampleDistUs' milliseconds
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pkL = merge_close_sample_points( pkL, analysisArgsD['minSampleDistUs'] )
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# split pkL
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pkDbL,pkUsL = tuple(zip(*pkL))
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# locate the first and last note
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min_pk_idx, max_pk_idx = find_min_max_peak_index( pkDbL, analysisArgsD['minAttkDb'], analysisArgsD['maxDbOffset'] )
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# estimate the microsecond locations to resample
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resampleUsSet = calc_resample_ranges( pkDbL, pkUsL, min_pk_idx, max_pk_idx, analysisArgsD['maxDeltaDb'], analysisArgsD['samplesPerDb'], analysisArgsD['minSampleDistUs'] )
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resampleUsL = sorted( list(resampleUsSet) )
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#print(resampleUsL)
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return resampleUsL, pkDbL, pkUsL
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def plot_resample_pulse_times( inDir, analysisArgsD ):
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newPulseUsL, rmsDbV, pulseUsL = form_resample_pulse_time_list( inDir, analysisArgsD )
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midi_pitch = int( inDir.split("/")[-1] )
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velTblUsL,velTblDbL,_ = form_final_pulse_list( inDir, midi_pitch, analysisArgsD, take_id=None )
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fig,ax = plt.subplots()
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ax.plot(pulseUsL,rmsDbV )
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for us in newPulseUsL:
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ax.axvline( x = us )
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ax.plot(velTblUsL,velTblDbL,marker='.',linestyle='None')
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plt.show()
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def find_min_max_peak_index( pkDbL, minDb, maxDbOffs ):
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"""
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Find the min db and max db peak.
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"""
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# select only the peaks from rmsV[] to work with
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yV = pkDbL
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# get the max volume note
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max_i = np.argmax( yV )
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maxDb = yV[ max_i ]
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min_i = max_i
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# starting from the max volume peak go backwards
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for i in range( max_i, 0, -1 ):
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# if this peak is within maxDbOffs of the loudest then choose this one instead
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#if maxDb - yV[i] < maxDbOffs:
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# max_i = i
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# if this peak is less than minDb then the previous note is the min note
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if yV[i] < minDb:
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break
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min_i = i
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if min_i >= max_i:
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min_i = 0
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max_i = len(pkDbL)-1
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if min_i == 0:
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print("No silent notes were generated. Decrease the minimum peak level or the hold voltage.")
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return min_i, max_i
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def find_skip_peaks( rmsV, pkIdxL, min_pk_idx, max_pk_idx ):
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""" Fine peaks associated with longer attacks pulses that are lower than peaks with a shorter attack pulse.
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These peaks indicate degenerate portions of the pulse/db curve which must be skipped during velocity table formation
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"""
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skipPkIdxL = []
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yV = rmsV[pkIdxL]
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refPkDb = yV[min_pk_idx]
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for i in range( min_pk_idx+1, max_pk_idx+1 ):
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if yV[i] > refPkDb:
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refPkDb = yV[i]
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else:
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skipPkIdxL.append(i)
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return skipPkIdxL
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def find_out_of_range_peaks( rmsV, pkIdxL, min_pk_idx, max_pk_idx, maxDeltaDb ):
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""" Locate peaks which are more than maxDeltaDb from the previous peak.
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If two peaks are separated by more than maxDeltaDb then the range must be resampled
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"""
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oorPkIdxL = []
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yV = rmsV[pkIdxL]
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for i in range( min_pk_idx, max_pk_idx+1 ):
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if i > 0:
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d = yV[i] - yV[i-1]
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if d > maxDeltaDb or d < 0:
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oorPkIdxL.append(i)
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return oorPkIdxL
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def plot_spectrum( ax, srate, binHz, specV, midiPitch, harmN ):
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""" Plot a single spectrum, 'specV' and the harmonic peak location boundaries."""
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binN = specV.shape[0]
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harmLBinL,harmMBinL,harmUBinL = calc_harm_bins( srate, binHz, midiPitch, harmN )
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fundHz = harmMBinL[0] * binHz
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maxPlotHz = fundHz * (harmN+1)
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maxPlotBinN = int(round(maxPlotHz/binHz))
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hzV = np.arange(binN) * (srate/(binN*2))
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specV = 20.0 * np.log10(specV)
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ax.plot(hzV[0:maxPlotBinN], specV[0:maxPlotBinN] )
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for h0,h1,h2 in zip(harmLBinL,harmMBinL,harmUBinL):
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ax.axvline( x=h0 * binHz, color="blue")
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ax.axvline( x=h1 * binHz, color="black")
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ax.axvline( x=h2 * binHz, color="blue")
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ax.set_ylabel(str(midiPitch))
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def plot_spectral_ranges( inDir, pitchL, rmsWndMs=300, rmsHopMs=30, harmN=5, dbRefWndMs=500 ):
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""" Plot the spectrum from one note (7th from last) in each attack pulse length sequence referred to by pitchL."""
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plotN = len(pitchL)
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fig,axL = plt.subplots(plotN,1)
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for plot_idx,midiPitch in enumerate(pitchL):
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# get the audio and meta-data file names
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seqFn = os.path.join( inDir, str(midiPitch), "seq.json")
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audioFn = os.path.join( inDir, str(midiPitch), "audio.wav")
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# read the meta data object
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with open( seqFn, "rb") as f:
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r = json.load(f)
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# read the audio file
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srate, signalM = wavfile.read(audioFn)
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sigV = signalM / float(0x7fff)
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# calc. the RMS envelope in the time domain
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rms0DbV, rms0_srate = audio_rms( srate, sigV, rmsWndMs, rmsHopMs, dbRefWndMs )
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# locate the sample index of the peak of each note attack
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pkIdx0L = locate_peak_indexes( rms0DbV, rms0_srate, r['eventTimeL'] )
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# select the 7th to last note for spectrum measurement
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#
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# TODO: come up with a better way to select the note to measure
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#
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spectrumSmpIdx = pkIdx0L[ len(pkIdx0L) - 7 ]
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# calc. the RMS envelope by taking the max spectral peak in each STFT window
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rmsDbV, rms_srate, specV, specHopIdx, binHz = audio_stft_rms( srate, sigV, rmsWndMs, rmsHopMs, dbRefWndMs, spectrumSmpIdx)
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# specV[] is the spectrum of the note at spectrumSmpIdx
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# plot the spectrum and the harmonic selection ranges
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plot_spectrum( axL[plot_idx], srate, binHz, specV, midiPitch, harmN )
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plt.show()
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def td_plot( ax, inDir, midi_pitch, id, analysisArgsD ):
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r = rms_analysis_main( inDir, midi_pitch, **analysisArgsD['rmsAnalysisArgs'] )
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min_pk_idx, max_pk_idx = find_min_max_peak_index( r.pkDbL, analysisArgsD['minAttkDb'], analysisArgsD['maxDbOffset'] )
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skipPkIdxL = find_skip_peaks( r.rmsDbV, r.pkIdxL, min_pk_idx, max_pk_idx )
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jmpPkIdxL = find_out_of_range_peaks( r.rmsDbV, r.pkIdxL, min_pk_idx, max_pk_idx, analysisArgsD['maxDeltaDb'] )
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secV = np.arange(0,len(r.rmsDbV)) / r.rms_srate
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ax.plot( secV, r.rmsDbV )
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ax.plot( np.arange(0,len(r.tdRmsDbV)) / r.rms_srate, r.tdRmsDbV, color="black" )
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# print beg/end boundaries
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for i,(begMs, endMs) in enumerate(r.eventTimeL):
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pkSec = r.pkIdxL[i] / r.rms_srate
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endSec = pkSec + r.statsL[i].durMs / 1000.0
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ax.axvline( x=begMs/1000.0, color="green")
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ax.axvline( x=endMs/1000.0, color="red")
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ax.axvline( x=pkSec, color="black")
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ax.axvline( x=endSec, color="black")
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ax.text(begMs/1000.0, 20.0, str(i) )
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# plot peak markers
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for i,pki in enumerate(r.pkIdxL):
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marker = 4 if i==min_pk_idx or i==max_pk_idx else 5
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color = "red" if i in skipPkIdxL else "black"
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ax.plot( [pki / r.rms_srate], [ r.rmsDbV[pki] ], marker=marker, color=color)
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if i in jmpPkIdxL:
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ax.plot( [pki / r.rms_srate], [ r.rmsDbV[pki] ], marker=6, color="blue")
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return r
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def do_td_plot( inDir, analysisArgs ):
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fig,axL = plt.subplots(3,1)
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fig.set_size_inches(18.5, 10.5, forward=True)
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id = int(inDir.split("/")[-1])
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midi_pitch = int(inDir.split("/")[-2])
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r = td_plot(axL[0],inDir,midi_pitch,id,analysisArgs)
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qualityV = np.array([ x.quality for x in r.statsL ]) * np.max(r.pkDbL)
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durMsV = np.array([ x.durMs for x in r.statsL ])
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avgV = np.array([ x.durAvgDb for x in r.statsL ])
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#durMsV[ durMsV < 400 ] = 0
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#durMsV = durMsV * np.max(r.pkDbL)/np.max(durMsV)
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#durMsV = durMsV / 100.0
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dV = np.diff(r.pkDbL) / r.pkDbL[1:]
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axL[1].plot( r.pkUsL, r.pkDbL, marker='.',label="pkDb" )
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axL[1].plot( r.pkUsL, qualityV, marker='.',label="quality" )
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axL[1].plot( r.pkUsL, avgV, marker='.',label="avgDb" )
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#axL[2].plot( r.pkUsL, durMsV, marker='.' )
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axL[2].plot( r.pkUsL[1:], dV, marker='.',label='d')
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axL[2].set_ylim([-1,1])
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axL[1].legend()
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sni = select_first_stable_note_by_dur( durMsV )
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if sni is not None:
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axL[1].plot( r.pkUsL[sni], r.pkDbL[sni], marker='*', color='red')
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sni = select_first_stable_note_by_delta_db( r.pkDbL )
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if sni is not None:
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axL[2].plot( r.pkUsL[sni], dV[sni-1], marker='*', color='red')
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|
|
|
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for i,s in enumerate(r.statsL):
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axL[1].text( r.pkUsL[i], r.pkDbL[i] + 1, "%i" % (i))
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|
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for i in range(1,len(r.pkUsL)):
|
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axL[2].text( r.pkUsL[i], dV[i-1], "%i" % (i))
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|
|
|
|
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plt.show()
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|
|
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def do_td_multi_plot( inDir, analysisArgs ):
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|
|
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midi_pitch = int(inDir.split("/")[-1])
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|
|
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dirL = os.listdir(inDir)
|
|
|
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fig,axL = plt.subplots(len(dirL),1)
|
|
|
|
|
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for id,(idir,ax) in enumerate(zip(dirL,axL)):
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|
|
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td_plot(ax, os.path.join(inDir,str(id)), midi_pitch, id, analysisArgs )
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|
|
|
plt.show()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
|
inDir = sys.argv[1]
|
|
cfgFn = sys.argv[2]
|
|
take_id = None if len(sys.argv)<4 else sys.argv[3]
|
|
|
|
cfg = parse_yaml_cfg( cfgFn )
|
|
|
|
if take_id is not None:
|
|
inDir = os.path.join(inDir,take_id)
|
|
do_td_plot(inDir,cfg.analysisArgs)
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else:
|
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#do_td_multi_plot(inDir,cfg.analysisArgs)
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|
|
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#plot_spectral_ranges( inDir, [ 24, 36, 48, 60, 72, 84, 96, 104] )
|
|
|
|
plot_resample_pulse_times( inDir, cfg.analysisArgs )
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|
|