piccal/rms_analysis.py

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