First working version.
This commit is contained in:
kpl
parent
ac9061a72d
commit
f69c71c498
@ -181,6 +181,14 @@ class AudioDevice(object):
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return Result(smpN)
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def buffer_sample_ms( self ):
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r = self.buffer_sample_count()
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if r:
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r.value = int(r.value * 1000.0 / self.srate)
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return r
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def linear_buffer( self ):
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smpN = self.buffer_sample_count()
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17
common.py
Normal file
17
common.py
Normal file
@ -0,0 +1,17 @@
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import yaml,types
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def parse_yaml_cfg( fn ):
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"""Parse the YAML configuration file."""
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cfg = None
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with open(fn,"r") as f:
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cfgD = yaml.load(f, Loader=yaml.FullLoader)
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cfg = types.SimpleNamespace(**cfgD['p_ac'])
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return cfg
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71
convert.py
71
convert.py
@ -4,7 +4,7 @@ from shutil import copyfile
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def event_times( eventTimeFn ):
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eventL = []
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velL = []
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with open(eventTimeFn,"r") as f:
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rdr = csv.reader(f)
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@ -14,21 +14,25 @@ def event_times( eventTimeFn ):
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beginMs = int(row[1])
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elif row[0] == 'key_down':
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key_downMs = int(row[1]) - beginMs
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vel = int(row[3])
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elif row[0] == 'key_up':
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key_upMs = row[1]
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eventL.append( [ key_downMs, key_downMs+1000 ] )
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velL.append( vel )
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return eventL
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return eventL,velL
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def pulse_lengths( pulseLenFn ):
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def pulse_lengths( pulseLenFn, velL ):
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with open(pulseLenFn,'rb') as f:
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d = pickle.load(f)
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msL = d['msL']
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# note: first posn in table is a multiplier
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return [ msL[i]*msL[0] for i in range(1,len(msL))]
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msL = [ msL[i]*msL[0] for i in range(1,len(msL))]
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usL = [ msL[vel-1] for vel in velL ]
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return usL
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def convert( inDir, outDir ):
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@ -41,40 +45,57 @@ def convert( inDir, outDir ):
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if os.path.isdir(idir):
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eventTimeFn = os.path.join( idir, "labels_0.csv" )
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id = 0
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while True:
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eventTimeL = event_times(eventTimeFn)
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eventTimeFn = os.path.join( idir, "labels_%i.csv" % (id) )
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pulseTimeFn = os.path.join( idir, "table_0.pickle")
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if not os.path.isfile( eventTimeFn ):
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break
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pulseUsL = pulse_lengths( pulseTimeFn )
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eventTimeL,velL = event_times(eventTimeFn)
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pitch = idir.split("/")[-1]
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pulseTimeFn = os.path.join( idir, "table_%i.pickle" % (id))
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pulseUsL = pulse_lengths( pulseTimeFn, velL )
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d = {
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"pulseUsL":pulseUsL,
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"pitchL":[ pitch ],
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"noteDurMs":1000,
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"pauseDurMs":0,
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"holdDutyPct":50,
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"eventTimeL":eventTimeL,
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"beginMs":0
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}
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pitch = idir.split("/")[-1]
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odir = os.path.join( outDir, pitch )
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if not os.path.isdir(odir):
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os.mkdir(odir)
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if not pitch.isdigit():
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break
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with open(os.path.join( odir, "seq.json" ),"w") as f:
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f.write(json.dumps( d ))
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d = {
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"pulseUsL":pulseUsL,
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"pitchL":[ pitch ],
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"noteDurMs":1000,
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"pauseDurMs":0,
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"holdDutyPct":50,
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"eventTimeL":eventTimeL,
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"beginMs":0
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}
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copyfile( os.path.join(idir,"audio_0.wav"), os.path.join(odir,"audio.wav"))
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odir = os.path.join( outDir, pitch )
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if not os.path.isdir(odir):
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os.mkdir(odir)
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odir = os.path.join( odir, "%i" % (id) )
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if not os.path.isdir(odir):
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os.mkdir(odir)
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with open(os.path.join( odir, "seq.json" ),"w") as f:
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f.write(json.dumps( d ))
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copyfile( os.path.join(idir,"audio_%i.wav" % (id)), os.path.join(odir,"audio.wav"))
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id += 1
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if __name__ == "__main__":
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inDir = "/home/kevin/temp/picadae_ac_2/full_map"
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outDir = "/home/kevin/temp/p_ac_3_cvt"
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outDir = "/home/kevin/temp/p_ac_3_cvt/full_map"
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#inDir = "/home/kevin/temp/picadae_ac_2/week_0"
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#outDir = "/home/kevin/temp/p_ac_3_cvt/week_0"
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convert( inDir, outDir )
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109
p_ac.py
109
p_ac.py
@ -1,4 +1,4 @@
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import sys,os,argparse,yaml,types,logging,select,time,json
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import sys,os,argparse,types,logging,select,time,json
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from datetime import datetime
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import multiprocessing
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@ -7,6 +7,8 @@ from multiprocessing import Process, Pipe
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from picadae_api import Picadae
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from AudioDevice import AudioDevice
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from result import Result
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from common import parse_yaml_cfg
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from plot_seq import form_resample_pulse_time_list
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class AttackPulseSeq:
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""" Sequence a fixed chord over a list of attack pulse lengths."""
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@ -26,8 +28,9 @@ class AttackPulseSeq:
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self.next_ms = 0 # Time of next event (note-on or note_off)
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self.eventTimeL = [] # Onset/offset time of each note [ [onset_ms,offset_ms] ]
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self.beginMs = 0
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self.playOnlyFl = False
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def start( self, ms, outDir, pitchL, pulseUsL ):
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def start( self, ms, outDir, pitchL, pulseUsL, playOnlyFl=False ):
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self.outDir = outDir # directory to write audio file and results
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self.pitchL = pitchL # chord to play
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self.pulseUsL = pulseUsL # one onset pulse length in microseconds per sequence element
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@ -35,16 +38,27 @@ class AttackPulseSeq:
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self.pulse_idx = 0
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self.state = 'note_on'
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self.next_ms = ms + 500 # wait for 500ms to play the first note (this will guarantee that there is some empty space in the audio file before the first note)
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self.eventTimeL = [[0,0]] * len(pulseUsL) # initialize the event time
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self.eventTimeL = [[0,0] for _ in range(len(pulseUsL))] # initialize the event time
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self.beginMs = ms
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self.audio.record_enable(True) # start recording audio
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self.playOnlyFl = playOnlyFl
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for pitch in pitchL:
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self.api.set_pwm( pitch, self.holdDutyPct )
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if not playOnlyFl:
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self.audio.record_enable(True) # start recording audio
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self.tick(ms) # play the first note
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def stop(self, ms):
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self._send_note_off() # be sure that all notes are actually turn-off
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self.audio.record_enable(False) # stop recording audio
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if not self.playOnlyFl:
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self.audio.record_enable(False) # stop recording audio
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self._disable() # disable this sequencer
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self._write() # write the results
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if not self.playOnlyFl:
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self._write() # write the results
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def is_enabled(self):
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return self.state is not None
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@ -75,7 +89,8 @@ class AttackPulseSeq:
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def _note_on( self, ms ):
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self.eventTimeL[ self.pulse_idx ][0] = ms - self.beginMs
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#self.eventTimeL[ self.pulse_idx ][0] = ms - self.beginMs
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self.eventTimeL[ self.pulse_idx ][0] = self.audio.buffer_sample_ms().value
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self.next_ms = ms + self.noteDurMs
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self.state = 'note_off'
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@ -84,7 +99,8 @@ class AttackPulseSeq:
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print("note-on:",pitch,self.pulse_idx)
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def _note_off( self, ms ):
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self.eventTimeL[ self.pulse_idx ][1] = ms - self.beginMs
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#self.eventTimeL[ self.pulse_idx ][1] = ms - self.beginMs
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self.eventTimeL[ self.pulse_idx ][1] = self.audio.buffer_sample_ms().value
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self.next_ms = ms + self.pauseDurMs
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self.state = 'note_on'
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@ -130,19 +146,17 @@ class CalibrateKeys:
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self.cfg = cfg
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self.seq = AttackPulseSeq( audioDev, api, noteDurMs=1000, pauseDurMs=1000, holdDutyPct=50 )
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self.label = None
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self.pulseUsL = None
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self.chordL = None
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self.pitch_idx = -1
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def start( self, ms, label, chordL, pulseUsL ):
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def start( self, ms, chordL, pulseUsL, playOnlyFl=False ):
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if len(chordL) > 0:
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self.label = label
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self.pulseUsL = pulseUsL
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self.chordL = chordL
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self.pitch_idx = -1
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self._start_next_chord( ms )
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self._start_next_chord( ms, playOnlyFl )
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def stop( self, ms ):
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@ -158,11 +172,12 @@ class CalibrateKeys:
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# if the sequencer is done playing
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if not self.seq.is_enabled():
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self._start_next_chord( ms ) # ... else start the next sequence
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self._start_next_chord( ms, self.seq.playOnlyFl ) # ... else start the next sequence
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return None
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def _start_next_chord( self, ms ):
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def _start_next_chord( self, ms, playOnlyFl ):
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self.pitch_idx += 1
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@ -179,14 +194,43 @@ class CalibrateKeys:
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os.mkdir( outDir )
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# form the output directory as "<label>_<pitch0>_<pitch1> ... "
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dirStr = self.label + "_" + "_".join([ str(pitch) for pitch in pitchL ])
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dirStr = "_".join([ str(pitch) for pitch in pitchL ])
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outDir = os.path.join(outDir, dirStr )
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# start the sequencer
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self.seq.start( ms, outDir, pitchL, self.pulseUsL )
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print(outDir)
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if not os.path.isdir(outDir):
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os.mkdir(outDir)
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# get the next available output directory id
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outDir_id = self._calc_next_out_dir_id( outDir )
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# if this is not the first time this note has been sampled then get the resample locations
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if outDir_id != 0:
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self.pulseUsL,_,_ = form_resample_pulse_time_list( outDir, self.cfg.analysisArgs )
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if playOnlyFl:
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self.pulseUsL,_ = form_final_pulse_list( outDir, pitchL[0], self.cfg.analysisArgs, take_id=None )
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outDir = os.path.join( outDir, str(outDir_id) )
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if not os.path.isdir(outDir):
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os.mkdir(outDir)
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# start the sequencer
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self.seq.start( ms, outDir, pitchL, self.pulseUsL, playOnlyFl )
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def _calc_next_out_dir_id( self, outDir ):
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id = 0
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while os.path.isdir( os.path.join(outDir,"%i" % id)):
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id += 1
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return id
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# This is the main application API it is running in a child process.
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class App:
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@ -208,7 +252,9 @@ class App:
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res = self.audioDev.setup(**cfg.audio)
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if res:
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if not res:
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self.audio_dev_list(0)
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else:
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self.api = Picadae( key_mapL=cfg.key_mapL)
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# wait for the letter 'a' to come back from the serial port
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@ -237,7 +283,11 @@ class App:
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def calibrate_keys_start( self, ms, pitchRangeL ):
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chordL = [ [pitch] for pitch in range(pitchRangeL[0], pitchRangeL[1]+1)]
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self.calibrate.start( ms, "full", chordL, cfg.full_pulseL )
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self.calibrate.start( ms, chordL, cfg.full_pulseL )
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def play_keys_start( self, ms, pitchRangeL ):
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chordL = [ [pitch] for pitch in range(pitchRangeL[0], pitchRangeL[1]+1)]
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self.calibrate.start( ms, chordL, cfg.full_pulseL, playOnlyFl=True )
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def calibrate_keys_stop( self, ms ):
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self.calibrate.stop(ms)
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@ -362,8 +412,9 @@ class Shell:
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'q':{ "func":'quit', "minN":0, "maxN":0, "help":"quit"},
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'?':{ "func":"_help", "minN":0, "maxN":0, "help":"Print usage text."},
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'a':{ "func":"audio_dev_list", "minN":0, "maxN":0, "help":"List the audio devices."},
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'c':{ "func":"calibrate_keys_start", "minN":1, "maxN":2, "help":"Calibrate a range of keys."},
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's':{ "func":"calibrate_keys_stop", "minN":0, "maxN":0, "help":"Stop key calibration"}
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'c':{ "func":"calibrate_keys_start", "minN":1, "maxN":2, "help":"Calibrate a range of keys. "},
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's':{ "func":"calibrate_keys_stop", "minN":0, "maxN":0, "help":"Stop key calibration"},
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'p':{ "func":"play_keys_start", "minN":1, "maxN":2, "help":"Play current calibration"}
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}
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def _help( self, _=None ):
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@ -405,7 +456,6 @@ class Shell:
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if d['minN'] != -1 and (d['minN'] > len(argL) or len(argL) > d['maxN']):
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return self._syntaxError("Argument count mismatch. {} is out of range:{} to {}".format(len(argL),d['minN'],d['maxN']))
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# call the command function
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if func:
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result = func(*argL)
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@ -489,21 +539,6 @@ def parse_args():
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return ap.parse_args()
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def parse_yaml_cfg( fn ):
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"""Parse the YAML configuration file."""
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cfg = None
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with open(fn,"r") as f:
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cfgD = yaml.load(f, Loader=yaml.FullLoader)
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cfg = types.SimpleNamespace(**cfgD['p_ac'])
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return cfg
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if __name__ == "__main__":
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24
p_ac.yml
24
p_ac.yml
@ -18,13 +18,31 @@
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# MeasureSeq args
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outDir: "~/temp/p_ac3",
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outDir: "~/temp/p_ac_3",
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noteDurMs: 1000,
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pauseDurMs: 1000,
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holdDutyPct: 50,
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#full_pulseL: [ 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 6500, 7000, 8000, 9000, 10000, 12000, 14000, 18000, 22000, 26000, 30000, 34000, 40000],
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full_pulseL: [ 18000, 22000, 26000, 30000, 34000, 40000],
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full_pulse0L: [ 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 6500, 7000, 8000, 9000, 10000, 12000, 14000, 18000, 22000, 26000, 30000, 34000, 40000],
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full_pulse1L: [ 10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000, 18000, 20000, 22000, 24000, 26000, 30000, 32000, 34000, 36000, 40000],
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full_pulseL: [ 10000, 10500, 11000, 11500, 12000, 12500, 13000, 13500, 14000, 14500, 15000, 15500, 16000, 16500, 17000, 17500, 18000, 18500, 20000, 22000, 24000, 26000, 30000, 32000, 34000, 36000, 40000],
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# RMS analysis args
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analysisArgs: {
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rmsAnalysisArgs: {
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rmsWndMs: 300, # length of the RMS measurment window
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rmsHopMs: 30, # RMS measurement inter window distance
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dbRefWndMs: 500, # length of initial portion of signal to use to calculate the dB reference level
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harmCandN: 5, # count of harmonic candidates to locate during harmonic based RMS analysis
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harmN: 3, # count of harmonics to use to calculate harmonic based RMS analysis
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},
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minAttkDb: 5.0, # threshold of silence level
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maxDbOffset: 0.5, # travel down the from the max. note level by at most this amount to locate the max. peak
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maxDeltaDb: 2.0, # maximum db change between volume samples (changes greater than this will trigger resampling)
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samplesPerDb: 4, # count of samples per dB to resample ranges whose range is less than maxDeltaDb
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minSampleDistUs: 500 # minimum distance between sample points in microseconds
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},
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key_mapL: [
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490
plot_seq.py
490
plot_seq.py
@ -1,8 +1,8 @@
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import os, sys, json
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from scipy.io import wavfile
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from scipy.signal import stft
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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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def is_nanV( xV ):
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@ -12,10 +12,265 @@ def is_nanV( xV ):
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return False
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def find_min_max_peak_index( rmsV, pkIdxL, minDb, maxDbOffs=0.5 ):
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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
|
||||
|
||||
return id
|
||||
|
||||
|
||||
def form_final_pulse_list( inDir, midi_pitch, analysisArgsD, take_id=None ):
|
||||
|
||||
# append the midi pitch to the input directory
|
||||
#inDir = os.path.join( inDir, "%i" % (midi_pitch))
|
||||
|
||||
if False:
|
||||
# determine the take id if none was given
|
||||
if take_id is None:
|
||||
take_id = _find_max_take_id( inDir )
|
||||
|
||||
inDir = os.path.join(inDir,"%i" % (take_id))
|
||||
|
||||
assert( os.path.isdir(inDir))
|
||||
|
||||
# analyze the requested take audio
|
||||
r = rms_analysis_main( inDir, midi_pitch, **analysisArgsD['rmsAnalysisArgs'] )
|
||||
|
||||
pkL = []
|
||||
# store the peaks in pkL[ (db,us) ]
|
||||
for db,us in zip(r.pkDbL,r.pkUsL):
|
||||
pkL.append( (db,us) )
|
||||
|
||||
# sort the peaks on increasing attack pulse microseconds
|
||||
pkL = sorted( pkL, key= lambda x: x[1] )
|
||||
|
||||
# split pkL
|
||||
pkDbL,pkUsL = tuple(zip(*pkL))
|
||||
|
||||
dirL = os.listdir(inDir)
|
||||
|
||||
pkL = []
|
||||
|
||||
# for each take in this directory
|
||||
for idir in dirL:
|
||||
|
||||
take_number = int(idir)
|
||||
|
||||
# analyze this takes audio and locate the note peaks
|
||||
r = rms_analysis_main( os.path.join(inDir,idir), midi_pitch, **analysisArgsD['rmsAnalysisArgs'] )
|
||||
|
||||
# store the peaks in pkL[ (db,us) ]
|
||||
for db,us in zip(r.pkDbL,r.pkUsL):
|
||||
pkL.append( (db,us) )
|
||||
|
||||
# sort the peaks on increasing attack pulse microseconds
|
||||
pkL = sorted( pkL, key= lambda x: x[1] )
|
||||
|
||||
# merge sample points that separated by less than 'minSampleDistUs' milliseconds
|
||||
pkL = merge_close_sample_points( pkL, analysisArgsD['minSampleDistUs'] )
|
||||
|
||||
# split pkL
|
||||
pkDbL,pkUsL = tuple(zip(*pkL))
|
||||
|
||||
#-------------------------------------------
|
||||
|
||||
# locate the first and last note
|
||||
min_pk_idx, max_pk_idx = find_min_max_peak_index( pkDbL, analysisArgsD['minAttkDb'], analysisArgsD['maxDbOffset'] )
|
||||
|
||||
db1 = pkDbL[ max_pk_idx ]
|
||||
db0 = pkDbL[ min_pk_idx ]
|
||||
|
||||
pulseUsL = []
|
||||
pulseDbL = []
|
||||
multValL = []
|
||||
for out_idx in range(128):
|
||||
|
||||
# calc the target volume
|
||||
db = db0 + (out_idx * (db1-db0)/127.0)
|
||||
|
||||
multi_value_count = 0
|
||||
|
||||
# look for the target between each of the sampled points
|
||||
for i in range(1,len(pkDbL)):
|
||||
|
||||
# if the target volume is between these two sample points
|
||||
if pkDbL[i-1] <= db and db < pkDbL[i]:
|
||||
|
||||
# if the target has not already been located
|
||||
if len(pulseUsL) == out_idx:
|
||||
|
||||
# interpolate the pulse time from between the sampled points
|
||||
frac = (db - pkDbL[i-1]) / (pkDbL[i] - pkDbL[i-1])
|
||||
us = pkUsL[i-1] + frac * (pkUsL[i] - pkUsL[i-1])
|
||||
db = pkDbL[i-1] + frac * (pkDbL[i] - pkDbL[i-1])
|
||||
pulseUsL.append(us)
|
||||
pulseDbL.append(db)
|
||||
|
||||
else:
|
||||
# this target db value was found between multiple sampled points
|
||||
# therefore the sampled volume function is not monotonic
|
||||
multi_value_count += 1
|
||||
|
||||
if multi_value_count > 0:
|
||||
multValL.append((out_idx,multi_value_count))
|
||||
|
||||
if len(multValL) > 0:
|
||||
print("Multi-value pulse locations were found during velocity table formation: ",multValL)
|
||||
|
||||
return pulseUsL,pulseDbL
|
||||
|
||||
|
||||
|
||||
def merge_close_sample_points( pkDbUsL, minSampleDistanceUs ):
|
||||
|
||||
avg0Us = np.mean(np.diff([ x[1] for x in pkDbUsL ]))
|
||||
n0 = len(pkDbUsL)
|
||||
|
||||
while True:
|
||||
us0 = None
|
||||
db0 = None
|
||||
|
||||
for i,(db,us) in enumerate(pkDbUsL):
|
||||
if i > 0 and us - us0 < minSampleDistanceUs:
|
||||
us1 = (us0 + us)/2
|
||||
db1 = (db0 + db)/2
|
||||
pkDbUsL[i-1] = (db1,us1)
|
||||
del pkDbUsL[i]
|
||||
break
|
||||
else:
|
||||
us0 = us
|
||||
db0 = db
|
||||
|
||||
if i+1 == len(pkDbUsL):
|
||||
break
|
||||
|
||||
avg1Us = np.mean(np.diff([ x[1] for x in pkDbUsL ]))
|
||||
|
||||
print("%i sample points deleted by merging close points." % (n0 - len(pkDbUsL)))
|
||||
print("Mean time between samples - before:%f after:%f " % (avg0Us,avg1Us))
|
||||
print("Min time between samples: %i " % (np.min(np.diff([x[1] for x in pkDbUsL]))))
|
||||
|
||||
return pkDbUsL
|
||||
|
||||
|
||||
|
||||
def calc_resample_ranges( pkDbL, pkUsL, min_pk_idx, max_pk_idx, maxDeltaDb, samplePerDb ):
|
||||
|
||||
if min_pk_idx == 0:
|
||||
print("No silent notes were generated. Decrease the minimum peak level or the hold voltage.")
|
||||
return None
|
||||
|
||||
resampleUsSet = set()
|
||||
refPkDb = pkDbL[min_pk_idx]
|
||||
|
||||
#pkDbL = pkDbL[ pkIdxL ]
|
||||
|
||||
for i in range( min_pk_idx, max_pk_idx+1 ):
|
||||
|
||||
d = pkDbL[i] - pkDbL[i-1]
|
||||
|
||||
usL = []
|
||||
|
||||
# if this peak is less than maxDeltaDb above the previous pk or
|
||||
# it is below the previous max peak
|
||||
if d > maxDeltaDb or d <= 0 or pkDbL[i] < refPkDb:
|
||||
|
||||
sampleCnt = max(int(round(abs(d) * samplePerDb)),samplePerDb)
|
||||
dUs = int(round((pkUsL[i] - pkUsL[i-1])/sampleCnt))
|
||||
usL = [ pkUsL[i-1] + dUs*j for j in range(sampleCnt)]
|
||||
|
||||
if i + 1 < len(pkDbL):
|
||||
d = pkDbL[i+1] - pkDbL[i]
|
||||
|
||||
sampleCnt = max(int(round(abs(d) * samplePerDb)),samplePerDb)
|
||||
dUs = int(round((pkUsL[i+1] - pkUsL[i])/sampleCnt))
|
||||
usL += [ pkUsL[i] + dUs*j for j in range(sampleCnt)]
|
||||
|
||||
if pkDbL[i] > refPkDb:
|
||||
refPkDb = pkDbL[i]
|
||||
|
||||
if usL:
|
||||
resampleUsSet = resampleUsSet.union( usL )
|
||||
|
||||
return resampleUsSet
|
||||
|
||||
|
||||
|
||||
def form_resample_pulse_time_list( inDir, analysisArgsD ):
|
||||
"""" This function merges all available data from previous takes to form
|
||||
a new list of pulse times to sample.
|
||||
"""
|
||||
|
||||
# the last folder is always the midi pitch of the note under analysis
|
||||
midi_pitch = int( inDir.split("/")[-1] )
|
||||
|
||||
dirL = os.listdir(inDir)
|
||||
|
||||
pkL = []
|
||||
|
||||
# for each take in this directory
|
||||
for idir in dirL:
|
||||
|
||||
take_number = int(idir)
|
||||
|
||||
# analyze this takes audio and locate the note peaks
|
||||
r = rms_analysis_main( os.path.join(inDir,idir), midi_pitch, **analysisArgsD['rmsAnalysisArgs'] )
|
||||
|
||||
# store the peaks in pkL[ (db,us) ]
|
||||
for db,us in zip(r.pkDbL,r.pkUsL):
|
||||
pkL.append( (db,us) )
|
||||
|
||||
# sort the peaks on increasing attack pulse microseconds
|
||||
pkL = sorted( pkL, key= lambda x: x[1] )
|
||||
|
||||
# merge sample points that separated by less than 'minSampleDistUs' milliseconds
|
||||
pkL = merge_close_sample_points( pkL, analysisArgsD['minSampleDistUs'] )
|
||||
|
||||
# split pkL
|
||||
pkDbL,pkUsL = tuple(zip(*pkL))
|
||||
|
||||
|
||||
# locate the first and last note
|
||||
min_pk_idx, max_pk_idx = find_min_max_peak_index( pkDbL, analysisArgsD['minAttkDb'], analysisArgsD['maxDbOffset'] )
|
||||
|
||||
# estimate the microsecond locations to resample
|
||||
resampleUsSet = calc_resample_ranges( pkDbL, pkUsL, min_pk_idx, max_pk_idx, analysisArgsD['maxDeltaDb'], analysisArgsD['samplesPerDb'] )
|
||||
|
||||
resampleUsL = sorted( list(resampleUsSet) )
|
||||
|
||||
return resampleUsL, pkDbL, pkUsL
|
||||
|
||||
|
||||
|
||||
def plot_resample_pulse_times( inDir, analysisArgsD ):
|
||||
|
||||
newPulseUsL, rmsDbV, pulseUsL = form_resample_pulse_time_list( inDir, analysisArgsD )
|
||||
|
||||
midi_pitch = int( inDir.split("/")[-1] )
|
||||
velTblUsL,velTblDbL = form_final_pulse_list( inDir, midi_pitch, analysisArgsD, take_id=None )
|
||||
|
||||
fig,ax = plt.subplots()
|
||||
|
||||
ax.plot(pulseUsL,rmsDbV )
|
||||
|
||||
for us in newPulseUsL:
|
||||
ax.axvline( x = us )
|
||||
|
||||
ax.plot(velTblUsL,velTblDbL,marker='.',linestyle='None')
|
||||
|
||||
plt.show()
|
||||
|
||||
def find_min_max_peak_index( pkDbL, minDb, maxDbOffs ):
|
||||
"""
|
||||
Find the min db and max db peak.
|
||||
"""
|
||||
# select only the peaks from rmsV[] to work with
|
||||
yV = rmsV[ pkIdxL ]
|
||||
yV = pkDbL
|
||||
|
||||
# get the max volume note
|
||||
max_i = np.argmax( yV )
|
||||
@ -38,9 +293,15 @@ def find_min_max_peak_index( rmsV, pkIdxL, minDb, maxDbOffs=0.5 ):
|
||||
|
||||
assert( min_i < max_i )
|
||||
|
||||
if min_i == 0:
|
||||
print("No silent notes were generated. Decrease the minimum peak level or the hold voltage.")
|
||||
|
||||
return min_i, max_i
|
||||
|
||||
def find_skip_peaks( rmsV, pkIdxL, min_pk_idx, max_pk_idx ):
|
||||
""" Fine peaks associated with longer attacks pulses that are lower than peaks with a shorter attack pulse.
|
||||
These peaks indicate degenerate portions of the pulse/db curve which must be skipped during velocity table formation
|
||||
"""
|
||||
skipPkIdxL = []
|
||||
yV = rmsV[pkIdxL]
|
||||
refPkDb = yV[min_pk_idx]
|
||||
@ -54,131 +315,22 @@ def find_skip_peaks( rmsV, pkIdxL, min_pk_idx, max_pk_idx ):
|
||||
|
||||
return skipPkIdxL
|
||||
|
||||
def find_out_of_range_peaks( rmsV, pkIdxL, min_pk_idx, max_pk_idx, maxDeltaDb ):
|
||||
""" Locate peaks which are more than maxDeltaDb from the previous peak.
|
||||
If two peaks are separated by more than maxDeltaDb then the range must be resampled
|
||||
"""
|
||||
|
||||
def calc_harm_bins( srate, binHz, midiPitch, harmN ):
|
||||
oorPkIdxL = []
|
||||
yV = rmsV[pkIdxL]
|
||||
|
||||
semi_tone = 1.0/12
|
||||
quarter_tone = 1.0/24
|
||||
eigth_tone = 1.0/48
|
||||
band_width_st = 3.0/48 # 3/8 tone
|
||||
for i in range( min_pk_idx, max_pk_idx+1 ):
|
||||
if i > 0:
|
||||
d = yV[i] - yV[i-1]
|
||||
if d > maxDeltaDb or d < 0:
|
||||
oorPkIdxL.append(i)
|
||||
|
||||
fundHz = (13.75 * pow(2.0,(-9.0/12.0))) * pow(2.0,(midiPitch / 12))
|
||||
fund_l_binL = [int(round(fundHz * pow(2.0,-band_width_st) * i/binHz)) for i in range(1,harmN+1)]
|
||||
fund_m_binL = [int(round(fundHz * i/binHz)) for i in range(1,harmN+1)]
|
||||
fund_u_binL = [int(round(fundHz * pow(2.0, band_width_st) * i/binHz)) for i in range(1,harmN+1)]
|
||||
return oorPkIdxL
|
||||
|
||||
for i in range(len(fund_m_binL)):
|
||||
if fund_l_binL[i] >= fund_m_binL[i] and fund_l_binL[i] > 0:
|
||||
fund_l_binL[i] = fund_m_binL[i] - 1
|
||||
|
||||
if fund_u_binL[i] <= fund_m_binL[i] and fund_u_binL[i] < len(fund_u_binL)-1:
|
||||
fund_u_binL[i] = fund_m_binL[i] + 1
|
||||
|
||||
return fund_l_binL, fund_m_binL, fund_u_binL
|
||||
|
||||
def rms_to_db( xV, rms_srate, refWndMs ):
|
||||
dbWndN = int(round(refWndMs * rms_srate / 1000.0))
|
||||
dbRef = ref = np.mean(xV[0:dbWndN])
|
||||
rmsDbV = 20.0 * np.log10( xV / dbRef )
|
||||
|
||||
return rmsDbV
|
||||
|
||||
def audio_rms( srate, xV, rmsWndMs, hopMs, refWndMs ):
|
||||
|
||||
wndSmpN = int(round( rmsWndMs * srate / 1000.0))
|
||||
hopSmpN = int(round( hopMs * srate / 1000.0))
|
||||
|
||||
xN = xV.shape[0]
|
||||
yN = int(((xN - wndSmpN) / hopSmpN) + 1)
|
||||
assert( yN > 0)
|
||||
yV = np.zeros( (yN, ) )
|
||||
|
||||
assert( wndSmpN > 1 )
|
||||
|
||||
i = 0
|
||||
j = 0
|
||||
while i < xN and j < yN:
|
||||
|
||||
if i == 0:
|
||||
yV[j] = np.sqrt(xV[0]*xV[0])
|
||||
elif i < wndSmpN:
|
||||
yV[j] = np.sqrt( np.mean( xV[0:i] * xV[0:i] ) )
|
||||
else:
|
||||
yV[j] = np.sqrt( np.mean( xV[i-wndSmpN:i] * xV[i-wndSmpN:i] ) )
|
||||
|
||||
i += hopSmpN
|
||||
j += 1
|
||||
|
||||
rms_srate = srate / hopSmpN
|
||||
return rms_to_db( yV, rms_srate, refWndMs ), rms_srate
|
||||
|
||||
|
||||
def audio_stft_rms( srate, xV, rmsWndMs, hopMs, refWndMs, spectrumIdx ):
|
||||
|
||||
wndSmpN = int(round( rmsWndMs * srate / 1000.0))
|
||||
hopSmpN = int(round( hopMs * srate / 1000.0))
|
||||
binHz = srate / wndSmpN
|
||||
|
||||
f,t,xM = stft( xV, fs=srate, window="hann", nperseg=wndSmpN, noverlap=wndSmpN-hopSmpN, return_onesided=True )
|
||||
|
||||
specHopIdx = int(round( spectrumIdx ))
|
||||
specV = np.sqrt(np.abs(xM[:, specHopIdx ]))
|
||||
|
||||
mV = np.zeros((xM.shape[1]))
|
||||
|
||||
for i in range(xM.shape[1]):
|
||||
mV[i] = np.max(np.sqrt(np.abs(xM[:,i])))
|
||||
|
||||
|
||||
rms_srate = srate / hopSmpN
|
||||
mV = rms_to_db( mV, rms_srate, refWndMs )
|
||||
|
||||
return mV, rms_srate, specV, specHopIdx, binHz
|
||||
|
||||
|
||||
def audio_harm_rms( srate, xV, rmsWndMs, hopMs, dbRefWndMs, midiPitch, harmCandN, harmN ):
|
||||
|
||||
wndSmpN = int(round( rmsWndMs * srate / 1000.0))
|
||||
hopSmpN = int(round( hopMs * srate / 1000.0))
|
||||
|
||||
binHz = srate / wndSmpN
|
||||
|
||||
f,t,xM = stft( xV, fs=srate, window="hann", nperseg=wndSmpN, noverlap=wndSmpN-hopSmpN, return_onesided=True )
|
||||
|
||||
harmLBinL,harmMBinL,harmUBinL = calc_harm_bins( srate, binHz, midiPitch, harmCandN )
|
||||
|
||||
rmsV = np.zeros((xM.shape[1],))
|
||||
|
||||
|
||||
for i in range(xM.shape[1]):
|
||||
mV = np.sqrt(np.abs(xM[:,i]))
|
||||
|
||||
pV = np.zeros((len(harmLBinL,)))
|
||||
|
||||
for j,(b0i,b1i) in enumerate(zip( harmLBinL, harmUBinL )):
|
||||
pV[j] = np.max(mV[b0i:b1i])
|
||||
|
||||
rmsV[i] = np.mean( sorted(pV)[-harmN:] )
|
||||
|
||||
|
||||
|
||||
rms_srate = srate / hopSmpN
|
||||
rmsV = rms_to_db( rmsV, rms_srate, dbRefWndMs )
|
||||
return rmsV, rms_srate, binHz
|
||||
|
||||
|
||||
|
||||
def locate_peak_indexes( xV, xV_srate, eventMsL ):
|
||||
|
||||
pkIdxL = []
|
||||
for begMs, endMs in eventMsL:
|
||||
|
||||
begSmpIdx = int(begMs * xV_srate / 1000.0)
|
||||
endSmpIdx = int(endMs * xV_srate / 1000.0)
|
||||
|
||||
pkIdxL.append( begSmpIdx + np.argmax( xV[begSmpIdx:endSmpIdx] ) )
|
||||
|
||||
return pkIdxL
|
||||
|
||||
|
||||
def plot_spectrum( ax, srate, binHz, specV, midiPitch, harmN ):
|
||||
@ -249,68 +401,82 @@ def plot_spectral_ranges( inDir, pitchL, rmsWndMs=300, rmsHopMs=30, harmN=5, dbR
|
||||
|
||||
|
||||
|
||||
def do_td_plot( inDir ):
|
||||
rmsWndMs = 300
|
||||
rmsHopMs = 30
|
||||
dbRefWndMs = 500
|
||||
harmCandN = 5
|
||||
harmN = 3
|
||||
minAttkDb = 5.0
|
||||
def td_plot( ax, inDir, midi_pitch, id, analysisArgsD ):
|
||||
|
||||
seqFn = os.path.join( inDir, "seq.json")
|
||||
audioFn = os.path.join( inDir, "audio.wav")
|
||||
midiPitch = int(inDir.split("/")[-1])
|
||||
r = rms_analysis_main( inDir, midi_pitch, **analysisArgsD['rmsAnalysisArgs'] )
|
||||
|
||||
min_pk_idx, max_pk_idx = find_min_max_peak_index( r.pkDbL, analysisArgsD['minAttkDb'], analysisArgsD['maxDbOffset'] )
|
||||
|
||||
with open( seqFn, "rb") as f:
|
||||
r = json.load(f)
|
||||
skipPkIdxL = find_skip_peaks( r.rmsDbV, r.pkIdxL, min_pk_idx, max_pk_idx )
|
||||
|
||||
jmpPkIdxL = find_out_of_range_peaks( r.rmsDbV, r.pkIdxL, min_pk_idx, max_pk_idx, analysisArgsD['maxDeltaDb'] )
|
||||
|
||||
srate, signalM = wavfile.read(audioFn)
|
||||
sigV = signalM / float(0x7fff)
|
||||
secV = np.arange(0,len(r.rmsDbV)) / r.rms_srate
|
||||
|
||||
rms0DbV, rms0_srate = audio_rms( srate, sigV, rmsWndMs, rmsHopMs, dbRefWndMs )
|
||||
ax.plot( secV, r.rmsDbV )
|
||||
ax.plot( np.arange(0,len(r.tdRmsDbV)) / r.rms_srate, r.tdRmsDbV, color="black" )
|
||||
|
||||
rmsDbV, rms_srate, binHz = audio_harm_rms( srate, sigV, rmsWndMs, rmsHopMs, dbRefWndMs, midiPitch, harmCandN, harmN )
|
||||
|
||||
pkIdxL = locate_peak_indexes( rmsDbV, rms_srate, r['eventTimeL'] )
|
||||
|
||||
|
||||
min_pk_idx, max_pk_idx = find_min_max_peak_index( rmsDbV, pkIdxL, minAttkDb )
|
||||
|
||||
skipPkIdxL = find_skip_peaks( rmsDbV, pkIdxL, min_pk_idx, max_pk_idx )
|
||||
|
||||
fig,ax = plt.subplots()
|
||||
fig.set_size_inches(18.5, 10.5, forward=True)
|
||||
|
||||
secV = np.arange(0,len(rmsDbV)) / rms_srate
|
||||
|
||||
ax.plot( secV, rmsDbV )
|
||||
ax.plot( np.arange(0,len(rms0DbV)) / rms0_srate, rms0DbV, color="black" )
|
||||
|
||||
# print beg/end boundaries
|
||||
for i,(begMs, endMs) in enumerate(r['eventTimeL']):
|
||||
for i,(begMs, endMs) in enumerate(r.eventTimeL):
|
||||
ax.axvline( x=begMs/1000.0, color="green")
|
||||
ax.axvline( x=endMs/1000.0, color="red")
|
||||
ax.text(begMs/1000.0, 20.0, str(i) )
|
||||
|
||||
return
|
||||
# plot peak markers
|
||||
for i,pki in enumerate(pkIdxL):
|
||||
marker = "o" if i==min_pk_idx or i==max_pk_idx else "."
|
||||
for i,pki in enumerate(r.pkIdxL):
|
||||
marker = 4 if i==min_pk_idx or i==max_pk_idx else 5
|
||||
color = "red" if i in skipPkIdxL else "black"
|
||||
ax.plot( [pki / rms_srate], [ rmsDbV[pki] ], marker=marker, color=color)
|
||||
ax.plot( [pki / r.rms_srate], [ r.rmsDbV[pki] ], marker=marker, color=color)
|
||||
|
||||
if i in jmpPkIdxL:
|
||||
ax.plot( [pki / r.rms_srate], [ r.rmsDbV[pki] ], marker=6, color="blue")
|
||||
|
||||
|
||||
|
||||
|
||||
def do_td_plot( inDir, analysisArgs ):
|
||||
|
||||
fig,ax = plt.subplots()
|
||||
fig.set_size_inches(18.5, 10.5, forward=True)
|
||||
|
||||
|
||||
id = int(inDir.split("/")[-1])
|
||||
midi_pitch = int(inDir.split("/")[-2])
|
||||
|
||||
td_plot(ax,inDir,midi_pitch,id,analysisArgs)
|
||||
|
||||
plt.show()
|
||||
|
||||
def do_td_multi_plot( inDir, analysisArgs ):
|
||||
|
||||
midi_pitch = int(inDir.split("/")[-1])
|
||||
|
||||
dirL = os.listdir(inDir)
|
||||
|
||||
fig,axL = plt.subplots(len(dirL),1)
|
||||
|
||||
|
||||
for id,(idir,ax) in enumerate(zip(dirL,axL)):
|
||||
|
||||
td_plot(ax, os.path.join(inDir,str(id)), midi_pitch, id, analysisArgs )
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
inDir = sys.argv[1]
|
||||
cfgFn = sys.argv[2]
|
||||
|
||||
do_td_plot(inDir)
|
||||
cfg = parse_yaml_cfg( cfgFn )
|
||||
|
||||
#do_td_plot(inDir,cfg.analysisArgs)
|
||||
|
||||
#o_td_multi_plot(inDir,cfg.analysisArgs)
|
||||
|
||||
#plot_spectral_ranges( inDir, [ 24, 36, 48, 60, 72, 84, 96, 104] )
|
||||
|
||||
plot_resample_pulse_times( inDir, cfg.analysisArgs )
|
||||
|
||||
|
||||
168
rms_analysis.py
Normal file
168
rms_analysis.py
Normal file
@ -0,0 +1,168 @@
|
||||
import os,types,json
|
||||
from scipy.io import wavfile
|
||||
from scipy.signal import stft
|
||||
import numpy as np
|
||||
|
||||
|
||||
def calc_harm_bins( srate, binHz, midiPitch, harmN ):
|
||||
|
||||
semi_tone = 1.0/12
|
||||
quarter_tone = 1.0/24
|
||||
eigth_tone = 1.0/48
|
||||
band_width_st = 3.0/48 # 3/8 tone
|
||||
|
||||
fundHz = (13.75 * pow(2.0,(-9.0/12.0))) * pow(2.0,(midiPitch / 12))
|
||||
fund_l_binL = [int(round(fundHz * pow(2.0,-band_width_st) * i/binHz)) for i in range(1,harmN+1)]
|
||||
fund_m_binL = [int(round(fundHz * i/binHz)) for i in range(1,harmN+1)]
|
||||
fund_u_binL = [int(round(fundHz * pow(2.0, band_width_st) * i/binHz)) for i in range(1,harmN+1)]
|
||||
|
||||
for i in range(len(fund_m_binL)):
|
||||
if fund_l_binL[i] >= fund_m_binL[i] and fund_l_binL[i] > 0:
|
||||
fund_l_binL[i] = fund_m_binL[i] - 1
|
||||
|
||||
if fund_u_binL[i] <= fund_m_binL[i] and fund_u_binL[i] < len(fund_u_binL)-1:
|
||||
fund_u_binL[i] = fund_m_binL[i] + 1
|
||||
|
||||
return fund_l_binL, fund_m_binL, fund_u_binL
|
||||
|
||||
def rms_to_db( xV, rms_srate, refWndMs ):
|
||||
dbWndN = int(round(refWndMs * rms_srate / 1000.0))
|
||||
dbRef = ref = np.mean(xV[0:dbWndN])
|
||||
rmsDbV = 20.0 * np.log10( xV / dbRef )
|
||||
|
||||
return rmsDbV
|
||||
|
||||
def audio_rms( srate, xV, rmsWndMs, hopMs, refWndMs ):
|
||||
|
||||
wndSmpN = int(round( rmsWndMs * srate / 1000.0))
|
||||
hopSmpN = int(round( hopMs * srate / 1000.0))
|
||||
|
||||
xN = xV.shape[0]
|
||||
yN = int(((xN - wndSmpN) / hopSmpN) + 1)
|
||||
assert( yN > 0)
|
||||
yV = np.zeros( (yN, ) )
|
||||
|
||||
assert( wndSmpN > 1 )
|
||||
|
||||
i = 0
|
||||
j = 0
|
||||
while i < xN and j < yN:
|
||||
|
||||
if i == 0:
|
||||
yV[j] = np.sqrt(xV[0]*xV[0])
|
||||
elif i < wndSmpN:
|
||||
yV[j] = np.sqrt( np.mean( xV[0:i] * xV[0:i] ) )
|
||||
else:
|
||||
yV[j] = np.sqrt( np.mean( xV[i-wndSmpN:i] * xV[i-wndSmpN:i] ) )
|
||||
|
||||
i += hopSmpN
|
||||
j += 1
|
||||
|
||||
rms_srate = srate / hopSmpN
|
||||
return rms_to_db( yV, rms_srate, refWndMs ), rms_srate
|
||||
|
||||
|
||||
def audio_stft_rms( srate, xV, rmsWndMs, hopMs, refWndMs, spectrumIdx ):
|
||||
|
||||
wndSmpN = int(round( rmsWndMs * srate / 1000.0))
|
||||
hopSmpN = int(round( hopMs * srate / 1000.0))
|
||||
binHz = srate / wndSmpN
|
||||
|
||||
f,t,xM = stft( xV, fs=srate, window="hann", nperseg=wndSmpN, noverlap=wndSmpN-hopSmpN, return_onesided=True )
|
||||
|
||||
specHopIdx = int(round( spectrumIdx ))
|
||||
specV = np.sqrt(np.abs(xM[:, specHopIdx ]))
|
||||
|
||||
mV = np.zeros((xM.shape[1]))
|
||||
|
||||
for i in range(xM.shape[1]):
|
||||
mV[i] = np.max(np.sqrt(np.abs(xM[:,i])))
|
||||
|
||||
|
||||
rms_srate = srate / hopSmpN
|
||||
mV = rms_to_db( mV, rms_srate, refWndMs )
|
||||
|
||||
return mV, rms_srate, specV, specHopIdx, binHz
|
||||
|
||||
|
||||
def audio_harm_rms( srate, xV, rmsWndMs, hopMs, dbRefWndMs, midiPitch, harmCandN, harmN ):
|
||||
|
||||
wndSmpN = int(round( rmsWndMs * srate / 1000.0))
|
||||
hopSmpN = int(round( hopMs * srate / 1000.0))
|
||||
|
||||
binHz = srate / wndSmpN
|
||||
|
||||
f,t,xM = stft( xV, fs=srate, window="hann", nperseg=wndSmpN, noverlap=wndSmpN-hopSmpN, return_onesided=True )
|
||||
|
||||
harmLBinL,harmMBinL,harmUBinL = calc_harm_bins( srate, binHz, midiPitch, harmCandN )
|
||||
|
||||
rmsV = np.zeros((xM.shape[1],))
|
||||
|
||||
|
||||
for i in range(xM.shape[1]):
|
||||
mV = np.sqrt(np.abs(xM[:,i]))
|
||||
|
||||
pV = np.zeros((len(harmLBinL,)))
|
||||
|
||||
for j,(b0i,b1i) in enumerate(zip( harmLBinL, harmUBinL )):
|
||||
pV[j] = np.max(mV[b0i:b1i])
|
||||
|
||||
rmsV[i] = np.mean( sorted(pV)[-harmN:] )
|
||||
|
||||
|
||||
|
||||
rms_srate = srate / hopSmpN
|
||||
rmsV = rms_to_db( rmsV, rms_srate, dbRefWndMs )
|
||||
return rmsV, rms_srate, binHz
|
||||
|
||||
|
||||
|
||||
def locate_peak_indexes( xV, xV_srate, eventMsL ):
|
||||
|
||||
pkIdxL = []
|
||||
for begMs, endMs in eventMsL:
|
||||
|
||||
begSmpIdx = int(begMs * xV_srate / 1000.0)
|
||||
endSmpIdx = int(endMs * xV_srate / 1000.0)
|
||||
|
||||
pkIdxL.append( begSmpIdx + np.argmax( xV[begSmpIdx:endSmpIdx] ) )
|
||||
|
||||
return pkIdxL
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def rms_analysis_main( inDir, midi_pitch, rmsWndMs=300, rmsHopMs=30, dbRefWndMs=500, harmCandN=5, harmN=3 ):
|
||||
|
||||
seqFn = os.path.join( inDir, "seq.json")
|
||||
audioFn = os.path.join( inDir, "audio.wav")
|
||||
|
||||
|
||||
with open( seqFn, "rb") as f:
|
||||
r = json.load(f)
|
||||
|
||||
|
||||
srate, signalM = wavfile.read(audioFn)
|
||||
sigV = signalM / float(0x7fff)
|
||||
|
||||
tdRmsDbV, rms0_srate = audio_rms( srate, sigV, rmsWndMs, rmsHopMs, dbRefWndMs )
|
||||
|
||||
rmsDbV, rms_srate, binHz = audio_harm_rms( srate, sigV, rmsWndMs, rmsHopMs, dbRefWndMs, midi_pitch, harmCandN, harmN )
|
||||
|
||||
pkIdxL = locate_peak_indexes( rmsDbV, rms_srate, r['eventTimeL'] )
|
||||
|
||||
r = types.SimpleNamespace(**{
|
||||
"audio_srate":srate,
|
||||
"tdRmsDbV": tdRmsDbV,
|
||||
"binHz": binHz,
|
||||
"rmsDbV":rmsDbV,
|
||||
"rms_srate":rms_srate,
|
||||
"pkIdxL":pkIdxL, # pkIdxL[ len(pulsUsL) ] - indexes into rmsDbV[] of peaks
|
||||
#"min_pk_idx":min_pk_idx,
|
||||
#"max_pk_idx":max_pk_idx,
|
||||
"eventTimeL":r['eventTimeL'],
|
||||
'pkDbL': [ rmsDbV[ i ] for i in pkIdxL ],
|
||||
'pkUsL':r['pulseUsL'] })
|
||||
|
||||
return r
|
||||
Loading…
Reference in New Issue
Block a user