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.
350 lines
9.6 KiB
Python
350 lines
9.6 KiB
Python
import os,types,json,pickle
|
|
from scipy.io import wavfile
|
|
from scipy.signal import stft
|
|
import numpy as np
|
|
from common import parse_yaml_cfg
|
|
|
|
|
|
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
|
|
|
|
#print( "STFT:", rmsWndMs, hopMs, wndSmpN, hopSmpN, wndSmpN-hopSmpN )
|
|
|
|
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 measure_duration_ms( rmsV, rms_srate, peak_idx, end_idx, decay_pct ):
|
|
"""
|
|
Calcuate the time it takes for a note to decay from the peak at
|
|
rmsV[peak_idx] dB to 'decay_pct' percent of the peak value.
|
|
"""
|
|
|
|
pkRmsDb = rmsV[ peak_idx ]
|
|
|
|
# calc the note turn-off (offset) db as a percentage of the peak amplitude
|
|
offsetRmsDb = pkRmsDb * decay_pct / 100.0
|
|
|
|
# calc the sample index where the note is off
|
|
offset_idx = peak_idx + np.argmin( np.abs(rmsV[peak_idx:end_idx] - offsetRmsDb) )
|
|
|
|
|
|
# calc the duration of the note
|
|
dur_ms = int(round((offset_idx - peak_idx) * 1000.0 / rms_srate))
|
|
|
|
#print(pkRmsDb, offsetRmsDb, peak_idx, offset_idx, end_idx, dur_ms, rms_srate)
|
|
|
|
return dur_ms
|
|
|
|
|
|
def select_first_stable_note_by_dur( durMsL, minDurMs=800 ):
|
|
|
|
first_stable_idx = None
|
|
for i,durMs in enumerate(durMsL):
|
|
if durMs > minDurMs and first_stable_idx is None:
|
|
first_stable_idx = i
|
|
else:
|
|
if durMs < minDurMs:
|
|
first_stable_idx = None
|
|
|
|
return first_stable_idx
|
|
|
|
def select_first_stable_note_by_delta_db_1( pkDbL, pkUsL, maxPulseUs=0.1 ):
|
|
|
|
wndN = 5
|
|
aL = []
|
|
dV = np.diff(pkDbL) / pkDbL[1:]
|
|
|
|
for ei in range(wndN,len(pkDbL)):
|
|
xV = dV[ei-wndN:ei]
|
|
avg = np.mean(np.abs(xV))
|
|
aL.append(avg)
|
|
|
|
k = np.argmin(np.abs(np.array(pkUsL) - maxPulseUs))
|
|
|
|
print(aL)
|
|
print(k)
|
|
|
|
|
|
for i in range(k,0,-1):
|
|
if aL[i] > maxDeltaDb:
|
|
return i + 1
|
|
|
|
return None
|
|
|
|
|
|
def select_first_stable_note_by_delta_db( pkDbL, pkUsL=None, maxPulseUs=0.1 ):
|
|
|
|
wndN = 5
|
|
|
|
dV = np.diff(pkDbL) / pkDbL[1:]
|
|
|
|
|
|
for ei in range(wndN,len(pkDbL)):
|
|
xV = dV[ei-wndN:ei]
|
|
avg = np.mean(np.abs(xV))
|
|
|
|
if avg < .1:
|
|
return (ei-wndN)+1
|
|
|
|
|
|
return None
|
|
|
|
def note_stats( r, decay_pct=50.0, extraDurSearchMs=500 ):
|
|
|
|
statsL = []
|
|
|
|
srate = r.rms_srate
|
|
|
|
qmax = 0
|
|
|
|
for i,(begSmpMs, endSmpMs) in enumerate(r.eventTimeL):
|
|
|
|
begSmpIdx = int(round(srate * begSmpMs / 1000.0))
|
|
endSmpIdx = int(round(srate * (endSmpMs + extraDurSearchMs) / 1000.0))
|
|
pkSmpIdx = r.pkIdxL[i]
|
|
|
|
durMs = measure_duration_ms( r.rmsDbV, srate, pkSmpIdx, endSmpIdx, decay_pct )
|
|
|
|
bi = pkSmpIdx
|
|
ei = pkSmpIdx + int(round(durMs * srate / 1000.0))
|
|
|
|
#bi = begSmpIdx
|
|
#ei = endSmpIdx
|
|
|
|
qualityCoeff = np.sum(r.rmsDbV[bi:ei]) + np.sum(r.tdRmsDbV[bi:ei])
|
|
if qualityCoeff > qmax:
|
|
qmax = qualityCoeff
|
|
|
|
durAvgDb = (np.mean(r.rmsDbV[bi:ei]) + np.mean(r.tdRmsDbV[bi:ei]))/2.0
|
|
|
|
statsL.append( types.SimpleNamespace(**{'begSmpSec':begSmpIdx/srate,'endSmpSec':endSmpIdx/srate,'pkSmpSec':pkSmpIdx/srate,'durMs':durMs, 'pkDb':r.pkDbL[i], 'pulse_us':r.pkUsL[i], 'quality':qualityCoeff, 'durAvgDb':durAvgDb }))
|
|
|
|
for i,r in enumerate(statsL):
|
|
statsL[i].quality /= qmax
|
|
|
|
|
|
return statsL
|
|
|
|
|
|
|
|
|
|
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 key_info_dictionary( keyMapL=None, yamlCfgFn=None):
|
|
|
|
if yamlCfgFn is not None:
|
|
cfg = parse_yaml_cfg(yamlCfgFn)
|
|
|
|
keyMapL = cfg.key_mapL
|
|
|
|
kmD = {}
|
|
for d in keyMapL:
|
|
kmD[ d['midi'] ] = types.SimpleNamespace(**d)
|
|
|
|
return kmD
|
|
|
|
|
|
def rms_analysis_main( inDir, midi_pitch, rmsWndMs=300, rmsHopMs=30, dbRefWndMs=500, harmCandN=5, harmN=3, durDecayPct=40 ):
|
|
|
|
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 )
|
|
|
|
tdPkIdxL = locate_peak_indexes( tdRmsDbV, rms0_srate, r['eventTimeL'])
|
|
|
|
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'] )
|
|
|
|
|
|
|
|
holdDutyPctL = None
|
|
if 'holdDutyPct' in r:
|
|
holdDutyPctL = [ (0, r['holdDutyPct']) ]
|
|
else:
|
|
holdDutyPctL = r['holdDutyPctL']
|
|
|
|
|
|
r = types.SimpleNamespace(**{
|
|
"audio_srate":srate,
|
|
"tdRmsDbV": tdRmsDbV,
|
|
"tdPkIdxL": tdPkIdxL,
|
|
"tdPkDbL": [ tdRmsDbV[i] for i in tdPkIdxL ],
|
|
"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'],
|
|
"holdDutyPctL":holdDutyPctL,
|
|
'pkDbL': [ rmsDbV[ i ] for i in pkIdxL ],
|
|
'pkUsL':r['pulseUsL'] })
|
|
|
|
|
|
statsL = note_stats(r,durDecayPct)
|
|
|
|
setattr(r,"statsL", statsL )
|
|
|
|
return r
|
|
|
|
|
|
def rms_analysis_main_all( inDir, cacheFn, rmsWndMs=300, rmsHopMs=30, dbRefWndMs=500, harmCandN=5, harmN=3, durDecayPct=40 ):
|
|
|
|
if os.path.isfile(cacheFn):
|
|
print("READING analysis cache file: %s" % (cacheFn))
|
|
with open(cacheFn,"rb") as f:
|
|
rD = pickle.load(f)
|
|
return rD
|
|
|
|
|
|
folderL = os.listdir(inDir)
|
|
|
|
rD = {}
|
|
|
|
for folder in folderL:
|
|
|
|
pathL = folder.split(os.sep)
|
|
|
|
midi_pitch = int(pathL[-1])
|
|
|
|
print(midi_pitch)
|
|
|
|
path = os.path.join(inDir,folder,'0')
|
|
|
|
if os.path.isdir(path) and os.path.isfile(os.path.join(os.path.join(path,"seq.json"))):
|
|
r = rms_analysis_main( path, midi_pitch, rmsWndMs=rmsWndMs, rmsHopMs=rmsHopMs, dbRefWndMs=dbRefWndMs, harmCandN=harmCandN, harmN=harmN, durDecayPct=durDecayPct )
|
|
|
|
rD[ midi_pitch ] = r
|
|
|
|
|
|
with open(cacheFn,"wb") as f:
|
|
pickle.dump(rD,f)
|
|
|
|
return rD
|