diff --git a/audfprint_analyze.py b/audfprint_analyze.py
index 3a73ec3..234bdb3 100644
--- a/audfprint_analyze.py
+++ b/audfprint_analyze.py
@@ -135,12 +135,12 @@ def __init__(self, density=DENSITY):
         # Limit the num of pairs we'll make from each peak (Fanout)
         self.maxpairsperpeak = 3
         # Values controlling peaks2landmarks
-        # +/- 31 bins in freq (LIMITED TO -32..31 IN LANDMARK2HASH)
-        self.targetdf = 31
+        # +/- 32 bins in freq (LIMITED TO -32..31 IN LANDMARK2HASH)
+        self.targetdf = 32
         # min time separation (traditionally 1, upped 2014-08-04)
         self.mindt = 2
         # max lookahead in time (LIMITED TO <64 IN LANDMARK2HASH)
-        self.targetdt = 63
+        self.targetdt = 65
         # global stores duration of most recently-read soundfile
         self.soundfiledur = 0.0
         # .. and total amount of sound processed
@@ -277,7 +277,7 @@ def find_peaks(self, d, sr):
         a_dec = (1 - 0.01 * (self.density * np.sqrt(self.n_hop / 352.8) / 35)) ** (1 / OVERSAMP)
         # Take spectrogram
         mywin = np.hanning(self.n_fft + 2)[1:-1]
-        sgram = np.abs(stft.stft(d, n_fft=self.n_fft,
+        sgram = np.abs(stft.stft(d, n_fft=self.n_fft+2,
                                  hop_length=self.n_hop,
                                  window=mywin))
         sgrammax = np.max(sgram)
@@ -289,10 +289,10 @@ def find_peaks(self, d, sr):
             # zero.  Not good, but let's let it through for now.
             print("find_peaks: Warning: input signal is identically zero.")
         # High-pass filter onset emphasis
-        # [:-1,] discards top bin (nyquist) of sgram so bins fit in 8 bits
+        # [1:-1,] discards bottom (0hz) and top (nyquist) bins of sgram so bins fit in 8 bits
         sgram = np.array([scipy.signal.lfilter([1, -1],
                                                [1, -HPF_POLE ** (1 / OVERSAMP)], s_row)
-                          for s_row in sgram])[:-1, ]
+                          for s_row in sgram])[1:-1, ]
         # Prune to keep only local maxima in spectrum that appear above an online,
         # decaying threshold
         peaks = self._decaying_threshold_fwd_prune(sgram, a_dec)
@@ -329,15 +329,16 @@ def peaks2landmarks(self, pklist):
                 for peak in peaks_at[col]:
                     pairsthispeak = 0
                     for col2 in range(col + self.mindt,
-                                       min(scols, col + self.targetdt)):
+                                      min(scols, col + self.targetdt + 1)):
                         if pairsthispeak < self.maxpairsperpeak:
                             for peak2 in peaks_at[col2]:
-                                if abs(peak2 - peak) < self.targetdf:
+                                df = peak2 - peak
+                                if -self.targetdf <= df and df < self.targetdf:
                                     # and abs(peak2-peak) + abs(col2-col) > 2 ):
                                     if pairsthispeak < self.maxpairsperpeak:
                                         # We have a pair!
                                         landmarks.append((col, peak,
-                                                          peak2, col2 - col))
+                                                          peak2, col2 - col - self.mindt))
                                         pairsthispeak += 1
 
         return landmarks