diff --git a/cover_song_simplified.py b/cover_song_simplified.py
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+## tutorial from: https://essentia.upf.edu/essentia_python_examples.html
+##In this tutorial, we use HPCP, ChromaCrossSimilarity and CoverSongSimilarity algorithms from essentia.
+
+import essentia.standard as estd
+from essentia.pytools.spectral import hpcpgram
+
+yesterday_original = 'audio/Yesterday (Remastered 2009).mp3'
+yesterday_cover_01 = 'audio/Yesterday - The Beatles - Connie Talbot (Cover).mp3'
+yesterday_cover_02 = 'audio/The Beatles - Yesterday Saxophone Cover Alexandra Ilieva Thomann.mp3'
+different_song = 'audio/Bella Poarch - Build a Btch (Official Music Video).mp3'
+
+# query cover song
+original_song = estd.MonoLoader(filename=yesterday_original, sampleRate=32000)()
+true_cover_01 = estd.MonoLoader(filename=yesterday_cover_01, sampleRate=32000)()
+true_cover_02 = estd.MonoLoader(filename=yesterday_cover_02, sampleRate=32000)()
+
+# wrong match
+false_cover_1 = estd.MonoLoader(filename=different_song, sampleRate=32000)()
+
+## Now let’s compute Harmonic Pitch Class Profile (HPCP) chroma features of these audio signals.
+query_hpcp = hpcpgram(original_song, sampleRate=32000)
+true_cover_hpcp_1 = hpcpgram(true_cover_01, sampleRate=32000)
+true_cover_hpcp_2 = hpcpgram(true_cover_02, sampleRate=32000)
+false_cover_hpcp = hpcpgram(false_cover_1, sampleRate=32000)
+
+## Next steps are done using the essentia ChromaCrossSimilarity function,
+## 
+##     Stacking input features
+## 
+##     Key invariance using Optimal Transposition Index (OTI) [3].
+## 
+##     Compute binary chroma cross similarity using cross recurrent plot as described in [1] or using OTI-based chroma binary method as detailed in [3]
+
+crp = estd.ChromaCrossSimilarity(frameStackSize=9,
+                                 frameStackStride=1,
+                                 binarizePercentile=0.095,
+                                 oti=True)
+
+true_pair_crp_1 = crp(query_hpcp, true_cover_hpcp_1)
+true_pair_crp_2 = crp(query_hpcp, true_cover_hpcp_2)
+
+## Compute binary chroma cross similarity using cross recurrent plot of the non-cover pairs
+
+false_pair_crp = crp(query_hpcp, false_cover_hpcp)
+
+## Alternatively, you can also use the OTI-based binary similarity method as explained in [2] to compute the cross similarity of two given chroma features.
+
+csm = estd.ChromaCrossSimilarity(frameStackSize=9,
+                                 frameStackStride=1,
+                                 binarizePercentile=0.095,
+                                 oti=True,
+                                 otiBinary=True)
+
+oti_csm = csm(query_hpcp, false_cover_hpcp)
+
+
+##     Finally, we compute an asymmetric cover song similarity measure from the pre-computed binary cross simialrity matrix of cover/non-cover pairs using various contraints of smith-waterman sequence alignment algorithm (eg. serra09 or chen17).
+## 
+## Computing cover song similarity distance between ‘Yesterday - accapella cover’ and ‘Yesterday - The Beatles’
+
+score_matrix, distance = estd.CoverSongSimilarity(disOnset=0.5,
+                                                  disExtension=0.5,
+                                                  alignmentType='serra09',
+                                                  distanceType='asymmetric')(true_pair_crp_1)
+print('Cover song similarity distance: %s' % distance)
+
+## other similar
+
+score_matrix, distance = estd.CoverSongSimilarity(disOnset=0.5,
+                                                  disExtension=0.5,
+                                                  alignmentType='serra09',
+                                                  distanceType='asymmetric')(true_pair_crp_2)
+print('Cover song similarity distance: %s' % distance)
+
+## Computing cover song similarity distance between Yesterday - accapella cover and Come Together cover - The Aerosmith.
+
+score_matrix, distance = estd.CoverSongSimilarity(disOnset=0.5,
+                                                  disExtension=0.5,
+                                                  alignmentType='serra09',
+                                                  distanceType='asymmetric')(false_pair_crp)
+print('Cover song similarity distance: %s' % distance)