diff --git a/README.md b/README.md
index 2062adb..8b4f9b4 100755
--- a/README.md
+++ b/README.md
@@ -100,6 +100,19 @@ An example configuration is as follows:
 >>> djv = Dejavu(config)
 ```
 
+## Tuning
+
+Inside `fingerprint.py`, you may want to adjust following parameters (some values are given below).
+
+    FINGERPRINT_REDUCTION = 30
+    PEAK_SORT = False
+    DEFAULT_OVERLAP_RATIO = 0.4
+    DEFAULT_FAN_VALUE = 10
+    DEFAULT_AMP_MIN = 15
+    PEAK_NEIGHBORHOOD_SIZE = 30
+    
+These parameters are described in the `fingerprint.py` in detail. Read that in-order to understand the impact of changing these values.
+
 ## Recognizing
 
 There are two ways to recognize audio using Dejavu. You can recognize by reading and processing files on disk, or through your computer's microphone.
diff --git a/dejavu/fingerprint.py b/dejavu/fingerprint.py
index a8a5aca..95c0076 100755
--- a/dejavu/fingerprint.py
+++ b/dejavu/fingerprint.py
@@ -12,8 +12,8 @@ IDX_TIME_J = 1
 
 ######################################################################
 # Sampling rate, related to the Nyquist conditions, which affects
-# the range frequencies we can detect. 
-DEFAULT_FS = 44100 
+# the range frequencies we can detect.
+DEFAULT_FS = 44100
 
 ######################################################################
 # Size of the FFT window, affects frequency granularity
@@ -23,15 +23,15 @@ 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  
+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 
+# higher will cause more fingerprints, but potentially better accuracy.
+DEFAULT_FAN_VALUE = 15
 
 ######################################################################
-# Minimum amplitude in spectrogram in order to be considered a peak. 
+# 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
@@ -39,13 +39,13 @@ 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. 
+# 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 
+# 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. 
+# DEFAULT_FAN_VALUE may not perform as expected.
 MIN_HASH_TIME_DELTA = 0
 MAX_HASH_TIME_DELTA = 200
 
@@ -56,7 +56,7 @@ MAX_HASH_TIME_DELTA = 200
 PEAK_SORT = True
 
 ######################################################################
-# Number of bits to throw away from the front of the SHA1 hash in the 
+# 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
@@ -137,7 +137,7 @@ def generate_hashes(peaks, fan_value=DEFAULT_FAN_VALUE):
     [(e05b341a9b77a51fd26, 32), ... ]
     """
     fingerprinted = set()  # to avoid rehashing same pairs
-    
+
     if PEAK_SORT:
         peaks.sort(key=itemgetter(1))