Added annotations for different fingerprinting parameters

dejavu/fingerprint.py

@@ -7,20 +7,60 @@ from scipy.ndimage.morphology import (generate_binary_structure,
 import hashlib
 from operator import itemgetter
 
-
 IDX_FREQ_I = 0
 IDX_TIME_J = 1
 
+######################################################################
+# Sampling rate, related to the Nyquist conditions, which affects
+# the range frequencies we can detect. 
 DEFAULT_FS = 44100 
+
+######################################################################
+# Size of the FFT window, affects frequency granularity
 DEFAULT_WINDOW_SIZE = 4096
+
+######################################################################
+# Ratio by which each sequential window overlaps the last and the
+# next window. Higher overlap will allow a higher granularity of offset
+# matching, but potentially more fingerprints.
 DEFAULT_OVERLAP_RATIO = 0.5  
+
+######################################################################
+# Degree to which a fingerprint can be paired with its neighbors --
+# higher will cause more fingerprints, but potentially better accuracy. 
 DEFAULT_FAN_VALUE = 15 
 
+######################################################################
+# Minimum amplitude in spectrogram in order to be considered a peak. 
+# This can be raised to reduce number of fingerprints, but can negatively
+# affect accuracy.
 DEFAULT_AMP_MIN = 10
+
+######################################################################
+# Number of cells around an amplitude peak in the spectrogram in order
+# for Dejavu to consider it a spectral peak. Higher values mean less
+# fingerprints and faster matching, but can potentially affect accuracy. 
 PEAK_NEIGHBORHOOD_SIZE = 20
+
+######################################################################
+# Thresholds on how close or far fingerprints can be in time in order 
+# to be paired as a fingerprint. If your max is too low, higher values of
+# DEFAULT_FAN_VALUE may not perform as expected. 
 MIN_HASH_TIME_DELTA = 0
 MAX_HASH_TIME_DELTA = 200
 
+######################################################################
+# If True, will sort peaks temporally for fingerprinting;
+# not sorting will cut down number of fingerprints, but potentially
+# affect performance.
+PEAK_SORT = True
+
+######################################################################
+# Number of bits to throw away from the front of the SHA1 hash in the 
+# fingerprint calculation. The more you throw away, the less storage, but
+# potentially higher collisions and misclassifications when identifying songs.
+FINGERPRINT_REDUCTION = 20
+
 def fingerprint(channel_samples, Fs=DEFAULT_FS,
                 wsize=DEFAULT_WINDOW_SIZE,
                 wratio=DEFAULT_OVERLAP_RATIO,
@@ -98,7 +138,8 @@ def generate_hashes(peaks, fan_value=DEFAULT_FAN_VALUE):
     """
     fingerprinted = set()  # to avoid rehashing same pairs
     
-    peaks.sort(key=itemgetter(1))
+    if PEAK_SORT:
+        peaks.sort(key=itemgetter(1))
 
     for i in range(len(peaks)):
         for j in range(1, fan_value):
@@ -114,7 +155,7 @@ def generate_hashes(peaks, fan_value=DEFAULT_FAN_VALUE):
                 if t_delta >= MIN_HASH_TIME_DELTA and t_delta <= MAX_HASH_TIME_DELTA:
                     h = hashlib.sha1(
                         "%s|%s|%s" % (str(freq1), str(freq2), str(t_delta)))
-                    yield (h.hexdigest()[0:20], t1)
+                    yield (h.hexdigest()[0:FINGERPRINT_REDUCTION], t1)
 
                 # ensure we don't repeat hashing
                 fingerprinted.add((i, i + j))