Added min/max volume note and skip note detection.

plot_seq.py

@@ -11,6 +11,49 @@ def is_nanV( xV ):
             return True
         
     return False
+
+def find_min_max_peak_index( rmsV, pkIdxL, minDb, maxDbOffs=0.5 ):
+
+    # select only the peaks from rmsV[] to work with
+    yV = rmsV[ pkIdxL ]
+
+    # get the max volume note
+    max_i = np.argmax( yV )
+    maxDb = yV[ max_i ]
+
+    min_i = max_i
+
+    # starting from the max volume peak go backwards
+    for i in range( max_i, 0, -1 ):
+
+        # if this peak is within maxDbOffs of the loudest then choose this one instead
+        if maxDb - yV[i] < maxDbOffs:
+            max_i = i
+
+        # if this peak is less than minDb then the previous note is the min note
+        if yV[i] < minDb:
+            break
+        
+        min_i = i
+
+    assert( min_i < max_i )
+    
+    return min_i, max_i
+
+def find_skip_peaks( rmsV, pkIdxL, min_pk_idx, max_pk_idx ):
+    skipPkIdxL = []
+    yV         = rmsV[pkIdxL]
+    refPkDb    = yV[min_pk_idx]
+
+    for i in range( min_pk_idx+1, max_pk_idx+1 ):
+        if yV[i] > refPkDb:
+            refPkDb = yV[i]
+        else:
+            skipPkIdxL.append(i)
+            
+            
+    return skipPkIdxL
+
         
 def calc_harm_bins( srate, binHz, midiPitch, harmN ):
 
@@ -33,9 +76,14 @@ def calc_harm_bins( srate, binHz, midiPitch, harmN ):
     
     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  ):
+def audio_rms( srate, xV, rmsWndMs, hopMs, refWndMs  ):
 
     wndSmpN = int(round( rmsWndMs * srate / 1000.0))
     hopSmpN = int(round( hopMs    * srate / 1000.0))
@@ -60,19 +108,12 @@ def audio_rms( srate, xV, rmsWndMs, hopMs  ):
 
         i += hopSmpN
         j += 1
-        
-    return yV, srate / hopSmpN
 
-def audio_db_rms( srate, xV, rmsWndMs, hopMs, dbRefWndMs ):
+    rms_srate = srate / hopSmpN
-
+    return rms_to_db( yV, rms_srate, refWndMs ), rms_srate
-    rmsV, rms_srate = audio_rms( srate, xV, rmsWndMs, hopMs )
-    dbWndN = int(round(dbRefWndMs * rms_srate / 1000.0))
-    dbRef = ref = np.mean(rmsV[0:dbWndN])
-    return 20.0 * np.log10( rmsV / dbRef ), rms_srate
 
 
-
+def audio_stft_rms( srate, xV, rmsWndMs, hopMs, refWndMs, spectrumIdx ):
-def audio_stft_rms( srate, xV, rmsWndMs, hopMs, spectrumIdx ):
     
     wndSmpN = int(round( rmsWndMs * srate / 1000.0))
     hopSmpN = int(round( hopMs    * srate / 1000.0))
@@ -88,18 +129,14 @@ def audio_stft_rms( srate, xV, rmsWndMs, hopMs, spectrumIdx ):
     for i in range(xM.shape[1]):
         mV[i] = np.max(np.sqrt(np.abs(xM[:,i])))
 
-    return mV, srate / hopSmpN, specV, specHopIdx, binHz
 
-def audio_stft_db_rms( srate, xV, rmsWndMs, hopMs, dbRefWndMs, spectrumIdx ):
+    rms_srate = srate / hopSmpN
-    rmsV, rms_srate, specV, specHopIdx, binHz = audio_stft_rms( srate, xV, rmsWndMs, hopMs, spectrumIdx )
+    mV = rms_to_db( mV, rms_srate, refWndMs )
-    
+        
-    dbWndN = int(round(dbRefWndMs * rms_srate / 1000.0))
+    return mV, rms_srate, specV, specHopIdx, binHz
-    dbRef = ref = np.mean(rmsV[0:dbWndN])
-    rmsDbV = 20.0 * np.log10( rmsV / dbRef )
 
-    return rmsDbV, rms_srate, specV, specHopIdx, binHz
 
-def audio_harm_rms( srate, xV, rmsWndMs, hopMs, midiPitch, harmCandN, harmN  ):
+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))
@@ -125,20 +162,9 @@ def audio_harm_rms( srate, xV, rmsWndMs, hopMs, midiPitch, harmCandN, harmN  ):
             
             
         
-
+    rms_srate = srate / hopSmpN
-    return rmsV, srate / hopSmpN, binHz
+    rmsV = rms_to_db( rmsV, rms_srate, dbRefWndMs )
-
+    return rmsV, rms_srate, binHz
-
-    
-def audio_harm_db_rms( srate, xV, rmsWndMs, hopMs, dbRefWndMs, midiPitch, harmCandN, harmN  ):
-    
-    rmsV, rms_srate, binHz = audio_harm_rms( srate, xV, rmsWndMs, hopMs, midiPitch, harmCandN, harmN )
-    
-    dbWndN = int(round(dbRefWndMs * rms_srate / 1000.0))
-    dbRef = ref = np.mean(rmsV[0:dbWndN])
-    rmsDbV = 20.0 * np.log10( rmsV / dbRef )
-
-    return rmsDbV, rms_srate, binHz
     
     
                
@@ -156,7 +182,8 @@ def locate_peak_indexes( xV, xV_srate, eventMsL ):
 
 
 def plot_spectrum( ax, srate, binHz, specV, midiPitch, harmN ):
-
+    """ Plot a single spectrum, 'specV' and the harmonic peak location boundaries."""
+    
     binN      = specV.shape[0]
     harmLBinL,harmMBinL,harmUBinL = calc_harm_bins( srate, binHz, midiPitch, harmN )
 
@@ -178,7 +205,8 @@ def plot_spectrum( ax, srate, binHz, specV, midiPitch, harmN ):
     ax.set_ylabel(str(midiPitch))
         
 def plot_spectral_ranges( inDir, pitchL, rmsWndMs=300, rmsHopMs=30, harmN=5, dbRefWndMs=500 ):
-
+    """ Plot the spectrum from one note (7th from last) in each attack pulse length sequence referred to by pitchL."""
+    
     plotN = len(pitchL)
     fig,axL = plt.subplots(plotN,1)
 
@@ -197,7 +225,7 @@ def plot_spectral_ranges( inDir, pitchL, rmsWndMs=300, rmsHopMs=30, harmN=5, dbR
         sigV  = signalM / float(0x7fff)
 
         # calc. the RMS envelope in the time domain
-        rms0DbV, rms0_srate = audio_db_rms( srate, sigV, rmsWndMs, rmsHopMs, dbRefWndMs )
+        rms0DbV, rms0_srate = audio_rms( srate, sigV, rmsWndMs, rmsHopMs, dbRefWndMs )
 
         # locate the sample index of the peak of each note attack 
         pkIdx0L = locate_peak_indexes( rms0DbV, rms0_srate, r['eventTimeL'] )
@@ -211,7 +239,7 @@ def plot_spectral_ranges( inDir, pitchL, rmsWndMs=300, rmsHopMs=30, harmN=5, dbR
 
 
         # calc. the RMS envelope by taking the max spectral peak in each STFT window 
-        rmsDbV, rms_srate, specV, specHopIdx, binHz = audio_stft_db_rms( srate, sigV, rmsWndMs, rmsHopMs, dbRefWndMs, spectrumSmpIdx)
+        rmsDbV, rms_srate, specV, specHopIdx, binHz = audio_stft_rms( srate, sigV, rmsWndMs, rmsHopMs, dbRefWndMs, spectrumSmpIdx)
 
         # specV[] is the spectrum of the note at spectrumSmpIdx
 
@@ -222,12 +250,12 @@ 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
     
     seqFn = os.path.join( inDir, "seq.json")
     audioFn = os.path.join( inDir, "audio.wav")
@@ -241,12 +269,17 @@ def do_td_plot( inDir ):
     srate, signalM  = wavfile.read(audioFn)
     sigV  = signalM / float(0x7fff)
         
-    rms0DbV, rms0_srate = audio_db_rms( srate, sigV, rmsWndMs, rmsHopMs, dbRefWndMs )
+    rms0DbV, rms0_srate = audio_rms( srate, sigV, rmsWndMs, rmsHopMs, dbRefWndMs )
 
-    rmsDbV, rms_srate, binHz = audio_harm_db_rms( srate, sigV, rmsWndMs, rmsHopMs, dbRefWndMs, midiPitch, harmCandN, harmN  )
+    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)
 
@@ -255,13 +288,17 @@ def do_td_plot( inDir ):
     ax.plot( secV, rmsDbV )
     ax.plot( np.arange(0,len(rms0DbV)) / rms0_srate, rms0DbV, color="black" )
 
-    for begMs, endMs in r['eventTimeL']:
+    # print beg/end boundaries
+    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) )
 
-        
+    # plot peak markers
     for i,pki in enumerate(pkIdxL):
-        ax.plot( [pki / rms_srate], [ rmsDbV[pki] ], marker='.', color="black")
+        marker = "o" if i==min_pk_idx or i==max_pk_idx else "."
+        color  = "red" if i in skipPkIdxL else "black"
+        ax.plot( [pki / rms_srate], [ rmsDbV[pki] ], marker=marker, color=color)
 
     plt.show()