In [6]:
%pylab inline
import glob
import matplotlib.pyplot as plt
import numexpr
import os
import pandas as pd
import seaborn as sns
Populating the interactive namespace from numpy and matplotlib
tools/add_pitch_class_sets.shD:dorian12*16 = 192 columns per row
In [3]:
def impute_missing_key_files():
path = 'data/beatles/keylab/The_Beatles/10CD2_-_The_Beatles/CD2_-_12_-_Revolution_9.lab'
if not os.path.exists(path):
df = pd.DataFrame.from_records(
[("0.0", "502.204082", "Silence", None)],
columns=['start', 'end', 'key_indicator', 'key_label'])
df.to_csv(path, sep='\t', index=None, header=None)
impute_missing_key_files()
In [4]:
files = glob.glob('data/beatles/keylab/The_Beatles/*/*.lab')
print('key files count:', len(files))
files
key files count: 180
Out[4]:
['data/beatles/keylab/The_Beatles/01_-_Please_Please_Me/01_-_I_Saw_Her_Standing_There.lab',
'data/beatles/keylab/The_Beatles/01_-_Please_Please_Me/02_-_Misery.lab',
'data/beatles/keylab/The_Beatles/01_-_Please_Please_Me/03_-_Anna_(Go_To_Him).lab',
'data/beatles/keylab/The_Beatles/01_-_Please_Please_Me/04_-_Chains.lab',
'data/beatles/keylab/The_Beatles/01_-_Please_Please_Me/05_-_Boys.lab',
'data/beatles/keylab/The_Beatles/01_-_Please_Please_Me/06_-_Ask_Me_Why.lab',
'data/beatles/keylab/The_Beatles/01_-_Please_Please_Me/07_-_Please_Please_Me.lab',
'data/beatles/keylab/The_Beatles/01_-_Please_Please_Me/08_-_Love_Me_Do.lab',
'data/beatles/keylab/The_Beatles/01_-_Please_Please_Me/09_-_P._S._I_Love_You.lab',
"data/beatles/keylab/The_Beatles/01_-_Please_Please_Me/10_-_Baby_It's_You.lab",
'data/beatles/keylab/The_Beatles/01_-_Please_Please_Me/11_-_Do_You_Want_To_Know_A_Secret.lab',
'data/beatles/keylab/The_Beatles/01_-_Please_Please_Me/12_-_A_Taste_Of_Honey.lab',
"data/beatles/keylab/The_Beatles/01_-_Please_Please_Me/13_-_There's_A_Place.lab",
'data/beatles/keylab/The_Beatles/01_-_Please_Please_Me/14_-_Twist_And_Shout.lab',
"data/beatles/keylab/The_Beatles/02_-_With_the_Beatles/01_-_It_Won't_Be_Long.lab",
"data/beatles/keylab/The_Beatles/02_-_With_the_Beatles/02_-_All_I've_Got_To_Do.lab",
'data/beatles/keylab/The_Beatles/02_-_With_the_Beatles/03_-_All_My_Loving.lab',
"data/beatles/keylab/The_Beatles/02_-_With_the_Beatles/04_-_Don't_Bother_Me.lab",
'data/beatles/keylab/The_Beatles/02_-_With_the_Beatles/05_-_Little_Child.lab',
'data/beatles/keylab/The_Beatles/02_-_With_the_Beatles/06_-_Till_There_Was_You.lab',
'data/beatles/keylab/The_Beatles/02_-_With_the_Beatles/07_-_Please_Mister_Postman.lab',
'data/beatles/keylab/The_Beatles/02_-_With_the_Beatles/08_-_Roll_Over_Beethoven.lab',
'data/beatles/keylab/The_Beatles/02_-_With_the_Beatles/09_-_Hold_Me_Tight.lab',
'data/beatles/keylab/The_Beatles/02_-_With_the_Beatles/10_-_You_Really_Got_A_Hold_On_Me.lab',
'data/beatles/keylab/The_Beatles/02_-_With_the_Beatles/11_-_I_Wanna_Be_Your_Man.lab',
'data/beatles/keylab/The_Beatles/02_-_With_the_Beatles/12_-_Devil_In_Her_Heart.lab',
'data/beatles/keylab/The_Beatles/02_-_With_the_Beatles/13_-_Not_A_Second_Time.lab',
'data/beatles/keylab/The_Beatles/02_-_With_the_Beatles/14_-_Money.lab',
"data/beatles/keylab/The_Beatles/03_-_A_Hard_Day's_Night/01_-_A_Hard_Day's_Night.lab",
"data/beatles/keylab/The_Beatles/03_-_A_Hard_Day's_Night/02_-_I_Should_Have_Known_Better.lab",
"data/beatles/keylab/The_Beatles/03_-_A_Hard_Day's_Night/03_-_If_I_Fell.lab",
"data/beatles/keylab/The_Beatles/03_-_A_Hard_Day's_Night/04_-_I'm_Happy_Just_To_Dance_With_You.lab",
"data/beatles/keylab/The_Beatles/03_-_A_Hard_Day's_Night/05_-_And_I_Love_Her.lab",
"data/beatles/keylab/The_Beatles/03_-_A_Hard_Day's_Night/06_-_Tell_Me_Why.lab",
"data/beatles/keylab/The_Beatles/03_-_A_Hard_Day's_Night/07_-_Can't_Buy_Me_Love.lab",
"data/beatles/keylab/The_Beatles/03_-_A_Hard_Day's_Night/08_-_Any_Time_At_All.lab",
"data/beatles/keylab/The_Beatles/03_-_A_Hard_Day's_Night/09_-_I'll_Cry_Instead.lab",
"data/beatles/keylab/The_Beatles/03_-_A_Hard_Day's_Night/10_-_Things_We_Said_Today.lab",
"data/beatles/keylab/The_Beatles/03_-_A_Hard_Day's_Night/11_-_When_I_Get_Home.lab",
"data/beatles/keylab/The_Beatles/03_-_A_Hard_Day's_Night/12_-_You_Can't_Do_That.lab",
"data/beatles/keylab/The_Beatles/03_-_A_Hard_Day's_Night/13_-_I'll_Be_Back.lab",
'data/beatles/keylab/The_Beatles/04_-_Beatles_for_Sale/01_-_No_Reply.lab',
"data/beatles/keylab/The_Beatles/04_-_Beatles_for_Sale/02_-_I'm_a_Loser.lab",
"data/beatles/keylab/The_Beatles/04_-_Beatles_for_Sale/03_-_Baby's_In_Black.lab",
'data/beatles/keylab/The_Beatles/04_-_Beatles_for_Sale/04_-_Rock_and_Roll_Music.lab',
"data/beatles/keylab/The_Beatles/04_-_Beatles_for_Sale/05_-_I'll_Follow_the_Sun.lab",
'data/beatles/keylab/The_Beatles/04_-_Beatles_for_Sale/06_-_Mr._Moonlight.lab',
'data/beatles/keylab/The_Beatles/04_-_Beatles_for_Sale/07_-_Kansas_City-_Hey,_Hey,_Hey,_Hey.lab',
'data/beatles/keylab/The_Beatles/04_-_Beatles_for_Sale/08_-_Eight_Days_a_Week.lab',
'data/beatles/keylab/The_Beatles/04_-_Beatles_for_Sale/09_-_Words_of_Love.lab',
"data/beatles/keylab/The_Beatles/04_-_Beatles_for_Sale/10_-_Honey_Don't.lab",
'data/beatles/keylab/The_Beatles/04_-_Beatles_for_Sale/11_-_Every_Little_Thing.lab',
"data/beatles/keylab/The_Beatles/04_-_Beatles_for_Sale/12_-_I_Don't_Want_to_Spoil_the_Party.lab",
"data/beatles/keylab/The_Beatles/04_-_Beatles_for_Sale/13_-_What_You're_Doing.lab",
"data/beatles/keylab/The_Beatles/04_-_Beatles_for_Sale/14_-_Everybody's_Trying_to_Be_My_Baby.lab",
'data/beatles/keylab/The_Beatles/05_-_Help!/01_-_Help!.lab',
'data/beatles/keylab/The_Beatles/05_-_Help!/02_-_The_Night_Before.lab',
"data/beatles/keylab/The_Beatles/05_-_Help!/03_-_You've_Got_To_Hide_Your_Love_Away.lab",
'data/beatles/keylab/The_Beatles/05_-_Help!/04_-_I_Need_You.lab',
'data/beatles/keylab/The_Beatles/05_-_Help!/05_-_Another_Girl.lab',
"data/beatles/keylab/The_Beatles/05_-_Help!/06_-_You're_Going_To_Lose_That_Girl.lab",
'data/beatles/keylab/The_Beatles/05_-_Help!/07_-_Ticket_To_Ride.lab',
'data/beatles/keylab/The_Beatles/05_-_Help!/08_-_Act_Naturally.lab',
"data/beatles/keylab/The_Beatles/05_-_Help!/09_-_It's_Only_Love.lab",
'data/beatles/keylab/The_Beatles/05_-_Help!/10_-_You_Like_Me_Too_Much.lab',
'data/beatles/keylab/The_Beatles/05_-_Help!/11_-_Tell_Me_What_You_See.lab',
"data/beatles/keylab/The_Beatles/05_-_Help!/12_-_I've_Just_Seen_a_Face.lab",
'data/beatles/keylab/The_Beatles/05_-_Help!/13_-_Yesterday.lab',
'data/beatles/keylab/The_Beatles/05_-_Help!/14_-_Dizzy_Miss_Lizzy.lab',
'data/beatles/keylab/The_Beatles/06_-_Rubber_Soul/01_-_Drive_My_Car.lab',
'data/beatles/keylab/The_Beatles/06_-_Rubber_Soul/02_-_Norwegian_Wood_(This_Bird_Has_Flown).lab',
"data/beatles/keylab/The_Beatles/06_-_Rubber_Soul/03_-_You_Won't_See_Me.lab",
'data/beatles/keylab/The_Beatles/06_-_Rubber_Soul/04_-_Nowhere_Man.lab',
'data/beatles/keylab/The_Beatles/06_-_Rubber_Soul/05_-_Think_For_Yourself.lab',
'data/beatles/keylab/The_Beatles/06_-_Rubber_Soul/06_-_The_Word.lab',
'data/beatles/keylab/The_Beatles/06_-_Rubber_Soul/07_-_Michelle.lab',
'data/beatles/keylab/The_Beatles/06_-_Rubber_Soul/08_-_What_Goes_On.lab',
'data/beatles/keylab/The_Beatles/06_-_Rubber_Soul/09_-_Girl.lab',
"data/beatles/keylab/The_Beatles/06_-_Rubber_Soul/10_-_I'm_Looking_Through_You.lab",
'data/beatles/keylab/The_Beatles/06_-_Rubber_Soul/11_-_In_My_Life.lab',
'data/beatles/keylab/The_Beatles/06_-_Rubber_Soul/12_-_Wait.lab',
'data/beatles/keylab/The_Beatles/06_-_Rubber_Soul/13_-_If_I_Needed_Someone.lab',
'data/beatles/keylab/The_Beatles/06_-_Rubber_Soul/14_-_Run_For_Your_Life.lab',
'data/beatles/keylab/The_Beatles/07_-_Revolver/01_-_Taxman.lab',
'data/beatles/keylab/The_Beatles/07_-_Revolver/02_-_Eleanor_Rigby.lab',
"data/beatles/keylab/The_Beatles/07_-_Revolver/03_-_I'm_Only_Sleeping.lab",
'data/beatles/keylab/The_Beatles/07_-_Revolver/04_-_Love_You_To.lab',
'data/beatles/keylab/The_Beatles/07_-_Revolver/05_-_Here,_There_And_Everywhere.lab',
'data/beatles/keylab/The_Beatles/07_-_Revolver/06_-_Yellow_Submarine.lab',
'data/beatles/keylab/The_Beatles/07_-_Revolver/07_-_She_Said_She_Said.lab',
'data/beatles/keylab/The_Beatles/07_-_Revolver/08_-_Good_Day_Sunshine.lab',
'data/beatles/keylab/The_Beatles/07_-_Revolver/09_-_And_Your_Bird_Can_Sing.lab',
'data/beatles/keylab/The_Beatles/07_-_Revolver/10_-_For_No_One.lab',
'data/beatles/keylab/The_Beatles/07_-_Revolver/11_-_Doctor_Robert.lab',
'data/beatles/keylab/The_Beatles/07_-_Revolver/12_-_I_Want_To_Tell_You.lab',
'data/beatles/keylab/The_Beatles/07_-_Revolver/13_-_Got_To_Get_You_Into_My_Life.lab',
'data/beatles/keylab/The_Beatles/07_-_Revolver/14_-_Tomorrow_Never_Knows.lab',
"data/beatles/keylab/The_Beatles/08_-_Sgt._Pepper's_Lonely_Hearts_Club_Band/01_-_Sgt._Pepper's_Lonely_Hearts_Club_Band.lab",
"data/beatles/keylab/The_Beatles/08_-_Sgt._Pepper's_Lonely_Hearts_Club_Band/02_-_With_A_Little_Help_From_My_Friends.lab",
"data/beatles/keylab/The_Beatles/08_-_Sgt._Pepper's_Lonely_Hearts_Club_Band/03_-_Lucy_In_The_Sky_With_Diamonds.lab",
"data/beatles/keylab/The_Beatles/08_-_Sgt._Pepper's_Lonely_Hearts_Club_Band/04_-_Getting_Better.lab",
"data/beatles/keylab/The_Beatles/08_-_Sgt._Pepper's_Lonely_Hearts_Club_Band/05_-_Fixing_A_Hole.lab",
"data/beatles/keylab/The_Beatles/08_-_Sgt._Pepper's_Lonely_Hearts_Club_Band/06_-_She's_Leaving_Home.lab",
"data/beatles/keylab/The_Beatles/08_-_Sgt._Pepper's_Lonely_Hearts_Club_Band/07_-_Being_For_The_Benefit_Of_Mr._Kite!.lab",
"data/beatles/keylab/The_Beatles/08_-_Sgt._Pepper's_Lonely_Hearts_Club_Band/08_-_Within_You_Without_You.lab",
"data/beatles/keylab/The_Beatles/08_-_Sgt._Pepper's_Lonely_Hearts_Club_Band/09_-_When_I'm_Sixty-Four.lab",
"data/beatles/keylab/The_Beatles/08_-_Sgt._Pepper's_Lonely_Hearts_Club_Band/10_-_Lovely_Rita.lab",
"data/beatles/keylab/The_Beatles/08_-_Sgt._Pepper's_Lonely_Hearts_Club_Band/11_-_Good_Morning_Good_Morning.lab",
"data/beatles/keylab/The_Beatles/08_-_Sgt._Pepper's_Lonely_Hearts_Club_Band/12_-_Sgt._Pepper's_Lonely_Hearts_Club_Band_(Reprise).lab",
"data/beatles/keylab/The_Beatles/08_-_Sgt._Pepper's_Lonely_Hearts_Club_Band/13_-_A_Day_In_The_Life.lab",
'data/beatles/keylab/The_Beatles/09_-_Magical_Mystery_Tour/01_-_Magical_Mystery_Tour.lab',
'data/beatles/keylab/The_Beatles/09_-_Magical_Mystery_Tour/02_-_The_Fool_On_The_Hill.lab',
'data/beatles/keylab/The_Beatles/09_-_Magical_Mystery_Tour/03_-_Flying.lab',
'data/beatles/keylab/The_Beatles/09_-_Magical_Mystery_Tour/04_-_Blue_Jay_Way.lab',
'data/beatles/keylab/The_Beatles/09_-_Magical_Mystery_Tour/05_-_Your_Mother_Should_Know.lab',
'data/beatles/keylab/The_Beatles/09_-_Magical_Mystery_Tour/06_-_I_Am_The_Walrus.lab',
'data/beatles/keylab/The_Beatles/09_-_Magical_Mystery_Tour/07_-_Hello_Goodbye.lab',
'data/beatles/keylab/The_Beatles/09_-_Magical_Mystery_Tour/08_-_Strawberry_Fields_Forever.lab',
'data/beatles/keylab/The_Beatles/09_-_Magical_Mystery_Tour/09_-_Penny_Lane.lab',
"data/beatles/keylab/The_Beatles/09_-_Magical_Mystery_Tour/10_-_Baby_You're_A_Rich_Man.lab",
'data/beatles/keylab/The_Beatles/09_-_Magical_Mystery_Tour/11_-_All_You_Need_Is_Love.lab',
'data/beatles/keylab/The_Beatles/10CD1_-_The_Beatles/CD1_-_01_-_Back_in_the_USSR.lab',
'data/beatles/keylab/The_Beatles/10CD1_-_The_Beatles/CD1_-_02_-_Dear_Prudence.lab',
'data/beatles/keylab/The_Beatles/10CD1_-_The_Beatles/CD1_-_03_-_Glass_Onion.lab',
'data/beatles/keylab/The_Beatles/10CD1_-_The_Beatles/CD1_-_04_-_Ob-La-Di,_Ob-La-Da.lab',
'data/beatles/keylab/The_Beatles/10CD1_-_The_Beatles/CD1_-_05_-_Wild_Honey_Pie.lab',
'data/beatles/keylab/The_Beatles/10CD1_-_The_Beatles/CD1_-_06_-_The_Continuing_Story_of_Bungalow_Bill.lab',
'data/beatles/keylab/The_Beatles/10CD1_-_The_Beatles/CD1_-_07_-_While_My_Guitar_Gently_Weeps.lab',
'data/beatles/keylab/The_Beatles/10CD1_-_The_Beatles/CD1_-_08_-_Happiness_is_a_Warm_Gun.lab',
'data/beatles/keylab/The_Beatles/10CD1_-_The_Beatles/CD1_-_09_-_Martha_My_Dear.lab',
"data/beatles/keylab/The_Beatles/10CD1_-_The_Beatles/CD1_-_10_-_I'm_So_Tired.lab",
'data/beatles/keylab/The_Beatles/10CD1_-_The_Beatles/CD1_-_11_-_Black_Bird.lab',
'data/beatles/keylab/The_Beatles/10CD1_-_The_Beatles/CD1_-_12_-_Piggies.lab',
'data/beatles/keylab/The_Beatles/10CD1_-_The_Beatles/CD1_-_13_-_Rocky_Raccoon.lab',
"data/beatles/keylab/The_Beatles/10CD1_-_The_Beatles/CD1_-_14_-_Don't_Pass_Me_By.lab",
"data/beatles/keylab/The_Beatles/10CD1_-_The_Beatles/CD1_-_15_-_Why_Don't_We_Do_It_In_The_Road.lab",
'data/beatles/keylab/The_Beatles/10CD1_-_The_Beatles/CD1_-_16_-_I_Will.lab',
'data/beatles/keylab/The_Beatles/10CD1_-_The_Beatles/CD1_-_17_-_Julia.lab',
'data/beatles/keylab/The_Beatles/10CD2_-_The_Beatles/CD2_-_01_-_Birthday.lab',
'data/beatles/keylab/The_Beatles/10CD2_-_The_Beatles/CD2_-_02_-_Yer_Blues.lab',
"data/beatles/keylab/The_Beatles/10CD2_-_The_Beatles/CD2_-_03_-_Mother_Nature's_Son.lab",
"data/beatles/keylab/The_Beatles/10CD2_-_The_Beatles/CD2_-_04_-_Everybody's_Got_Something_To_Hide_Except_Me_and_My_Monkey.lab",
'data/beatles/keylab/The_Beatles/10CD2_-_The_Beatles/CD2_-_05_-_Sexy_Sadie.lab',
'data/beatles/keylab/The_Beatles/10CD2_-_The_Beatles/CD2_-_06_-_Helter_Skelter.lab',
'data/beatles/keylab/The_Beatles/10CD2_-_The_Beatles/CD2_-_07_-_Long_Long_Long.lab',
'data/beatles/keylab/The_Beatles/10CD2_-_The_Beatles/CD2_-_08_-_Revolution_1.lab',
'data/beatles/keylab/The_Beatles/10CD2_-_The_Beatles/CD2_-_09_-_Honey_Pie.lab',
'data/beatles/keylab/The_Beatles/10CD2_-_The_Beatles/CD2_-_10_-_Savoy_Truffle.lab',
'data/beatles/keylab/The_Beatles/10CD2_-_The_Beatles/CD2_-_11_-_Cry_Baby_Cry.lab',
'data/beatles/keylab/The_Beatles/10CD2_-_The_Beatles/CD2_-_12_-_Revolution_9.lab',
'data/beatles/keylab/The_Beatles/10CD2_-_The_Beatles/CD2_-_13_-_Good_Night.lab',
'data/beatles/keylab/The_Beatles/11_-_Abbey_Road/01_-_Come_Together.lab',
'data/beatles/keylab/The_Beatles/11_-_Abbey_Road/02_-_Something.lab',
"data/beatles/keylab/The_Beatles/11_-_Abbey_Road/03_-_Maxwell's_Silver_Hammer.lab",
'data/beatles/keylab/The_Beatles/11_-_Abbey_Road/04_-_Oh!_Darling.lab',
"data/beatles/keylab/The_Beatles/11_-_Abbey_Road/05_-_Octopus's_Garden.lab",
'data/beatles/keylab/The_Beatles/11_-_Abbey_Road/06_-_I_Want_You.lab',
'data/beatles/keylab/The_Beatles/11_-_Abbey_Road/07_-_Here_Comes_The_Sun.lab',
'data/beatles/keylab/The_Beatles/11_-_Abbey_Road/08_-_Because.lab',
'data/beatles/keylab/The_Beatles/11_-_Abbey_Road/09_-_You_Never_Give_Me_Your_Money.lab',
'data/beatles/keylab/The_Beatles/11_-_Abbey_Road/10_-_Sun_King.lab',
'data/beatles/keylab/The_Beatles/11_-_Abbey_Road/11_-_Mean_Mr_Mustard.lab',
'data/beatles/keylab/The_Beatles/11_-_Abbey_Road/12_-_Polythene_Pam.lab',
'data/beatles/keylab/The_Beatles/11_-_Abbey_Road/13_-_She_Came_In_Through_The_Bathroom_Window.lab',
'data/beatles/keylab/The_Beatles/11_-_Abbey_Road/14_-_Golden_Slumbers.lab',
'data/beatles/keylab/The_Beatles/11_-_Abbey_Road/15_-_Carry_That_Weight.lab',
'data/beatles/keylab/The_Beatles/11_-_Abbey_Road/16_-_The_End.lab',
'data/beatles/keylab/The_Beatles/11_-_Abbey_Road/17_-_Her_Majesty.lab',
'data/beatles/keylab/The_Beatles/12_-_Let_It_Be/01_-_Two_of_Us.lab',
'data/beatles/keylab/The_Beatles/12_-_Let_It_Be/02_-_Dig_a_Pony.lab',
'data/beatles/keylab/The_Beatles/12_-_Let_It_Be/03_-_Across_the_Universe.lab',
'data/beatles/keylab/The_Beatles/12_-_Let_It_Be/04_-_I_Me_Mine.lab',
'data/beatles/keylab/The_Beatles/12_-_Let_It_Be/05_-_Dig_It.lab',
'data/beatles/keylab/The_Beatles/12_-_Let_It_Be/06_-_Let_It_Be.lab',
'data/beatles/keylab/The_Beatles/12_-_Let_It_Be/07_-_Maggie_Mae.lab',
"data/beatles/keylab/The_Beatles/12_-_Let_It_Be/08_-_I've_Got_A_Feeling.lab",
'data/beatles/keylab/The_Beatles/12_-_Let_It_Be/09_-_One_After_909.lab',
'data/beatles/keylab/The_Beatles/12_-_Let_It_Be/10_-_The_Long_and_Winding_Road.lab',
'data/beatles/keylab/The_Beatles/12_-_Let_It_Be/11_-_For_You_Blue.lab',
'data/beatles/keylab/The_Beatles/12_-_Let_It_Be/12_-_Get_Back.lab']
In [5]:
def read_key_file(path):
return pd.read_csv(path, sep='\t', header=None, names=['start','end','key_indicator','key_label'])
In [6]:
read_key_file(files[0])
Out[6]:
start
end
key_indicator
key_label
0
0.000
1.035
Silence
NaN
1
1.035
171.880
Key
E
2
171.880
175.800
Silence
NaN
In [7]:
def add_track_id(df, track_id):
df['track_id'] = track_id
return df
In [8]:
keys = pd.concat(add_track_id(read_key_file(file), track_id) for (track_id, file) in enumerate(files))
In [9]:
keys
Out[9]:
start
end
key_indicator
key_label
track_id
0
0.000
1.035
Silence
NaN
0
1
1.035
171.880
Key
E
0
2
171.880
175.800
Silence
NaN
0
0
0.000
0.395
Silence
NaN
1
1
0.395
105.685
Key
C
1
2
105.685
110.160
Silence
NaN
1
0
0.000
0.410
Silence
NaN
2
1
0.410
174.120
Key
D
2
2
174.120
177.573
Silence
NaN
2
0
0.000
146.493
Key
Bb
3
0
0.000
147.507
Key
E
4
0
0.000
147.760
Key
E
5
0
0.000
123.373
Key
E
6
0
0.000
142.760
Key
G
7
0
0.000
125.733
Key
D:aeolian
8
0
0.000
158.067
Key
G
9
0
0.000
119.333
Key
E
10
0
0.000
125.133
Key
F#:minor
11
0
0.000
112.867
Key
E
12
0
0.000
153.267
Key
D
13
0
0.000
1.030
Silence
NaN
14
1
1.030
129.940
Key
E
14
2
129.940
133.733
Silence
NaN
14
0
0.000
0.429
Silence
NaN
15
1
0.429
120.271
Key
E
15
2
120.271
124.533
Silence
NaN
15
0
0.000
0.383
Silence
NaN
16
1
0.383
125.530
Key
E
16
2
125.530
129.560
Silence
NaN
16
0
0.000
0.430
Silence
NaN
17
...
...
...
...
...
...
2
229.350
234.800
Silence
NaN
169
0
0.000
0.355
Silence
NaN
170
1
0.355
222.650
Key
D
170
2
222.650
228.560
Silence
NaN
170
0
0.000
0.375
Silence
NaN
171
1
0.375
144.340
Key
A:minor
171
2
144.340
145.733
Silence
NaN
171
0
0.000
1.480
Silence
NaN
172
1
1.480
50.000
Key
F
172
2
50.000
50.000
Silence
NaN
172
0
0.000
241.355
Key
C
173
1
241.355
243.307
Silence
NaN
173
0
0.000
0.345
Silence
NaN
174
1
0.345
38.005
Key
G
174
2
38.005
40.600
Silence
NaN
174
0
0.000
0.395
Silence
NaN
175
1
0.395
214.135
Key
A
175
2
214.135
217.960
Silence
NaN
175
0
0.000
0.350
Silence
NaN
176
1
0.350
170.535
Key
B
176
2
170.535
175.507
Silence
NaN
176
0
0.000
0.345
Silence
NaN
177
1
0.345
215.990
Key
Eb
177
2
215.990
217.867
Silence
NaN
177
0
0.000
0.450
Silence
NaN
178
1
0.450
149.205
Key
D
178
2
149.205
152.733
Silence
NaN
178
0
0.000
0.365
Silence
NaN
179
1
0.365
185.355
Key
A:mixolydian
179
2
185.355
187.093
Silence
NaN
179
408 rows × 5 columns
In [10]:
keys['duration'] = keys['end'] - keys['start']
In [11]:
keys['key_label'].value_counts()
Out[11]:
A 51
G 40
E 35
C 28
A:minor 25
D 25
F 13
E:minor 11
D:minor 9
C:minor 8
B 6
Eb 6
Bb 5
F:minor 5
C#:minor 4
F#:minor 2
G:mixolydian 2
Db 1
F:dorian 1
D:aeolian 1
A:mixolydian 1
C#:modal 1
F# 1
D:mixolydian 1
Eb:minor 1
Ab 1
dtype: int64
In [12]:
print('total number of key segments:', len(keys))
total number of key segments: 408
In [13]:
keys['duration'].describe()
Out[13]:
count 408.000000
mean 71.893412
std 81.089744
min -0.043000
25% 3.849000
50% 27.304000
75% 143.846750
max 502.204082
Name: duration, dtype: float64
In [14]:
sns.distplot(keys['duration'], bins=50)
title('distribution of key segment duration (sec)');
In [15]:
keys['key_indicator'].value_counts()
Out[15]:
Key 284
Silence 124
dtype: int64
In [16]:
# Total duration of segments with some key and with silence:
keys.groupby('key_indicator').sum()['duration']
Out[16]:
key_indicator
Key 28589.011000
Silence 743.501082
Name: duration, dtype: float64
In [17]:
# The same in percentage:
keys.groupby('key_indicator').sum()['duration'] / keys['duration'].sum() * 100
Out[17]:
key_indicator
Key 97.465266
Silence 2.534734
Name: duration, dtype: float64
Distribution of number of key segments among songs.
In [18]:
keys.groupby('track_id').count()['start'].describe()
Out[18]:
count 180.000000
mean 2.266667
std 2.372091
min 1.000000
25% 1.000000
50% 1.000000
75% 3.000000
max 18.000000
Name: start, dtype: float64
We need to map symbolic key labels to pitch classes. Since the label are not always referring to the diatonic key but sometimes to modes, we normalize the pitch class to represent the underlying diatonic key. This would help in our further classification since it reduces the number of classes and their meaning. In order key we can limit ourselves not to discriminate between modes.
In [19]:
diatonic_pitch_classes = {'C': 0, 'D': 2, 'E': 4, 'F': 5, 'G': 7, 'A': 9, 'B': 11}
accidental_shifts = {'': 0, 'b': -1, '#': 1}
mode_shifts = {
'': 0,
'major': 0,
'ionian': 0,
'dorian': -2,
'phrygian': -4,
'lydian': -5,
'mixolydian': -7,
'aeolian': -9,
'minor': -9,
'locrian': -11}
def tone_label_to_pitch_class(label):
base = label[0].upper()
pitch_class = diatonic_pitch_classes[base]
accidental = label[1] if len(label) > 1 else ''
shift = accidental_shifts[accidental]
return ((pitch_class + shift) + 12) % 12
def diatonic_root_for_key_label(key_label):
if type(key_label) is not str:
return
parts = key_label.split(':')
modal_root_label = parts[0]
modal_root_pitch_class = tone_label_to_pitch_class(modal_root_label)
mode_label = parts[1].lower() if len(parts) > 1 else ''
mode_shift = mode_shifts[mode_label] if mode_label in mode_shifts else 0
diatonic_root = modal_root_pitch_class + mode_shift
# return (modal_root_label, mode_label, modal_root_pitch_class, mode_shift, diatonic_root)
return (diatonic_root + 12) % 12
unique_key_labels = list(keys['key_label'].value_counts().index)
[(label, diatonic_root_for_key_label(label)) for label in unique_key_labels]
Out[19]:
[('A', 9),
('G', 7),
('E', 4),
('C', 0),
('A:minor', 0),
('D', 2),
('F', 5),
('E:minor', 7),
('D:minor', 5),
('C:minor', 3),
('B', 11),
('Eb', 3),
('Bb', 10),
('F:minor', 8),
('C#:minor', 4),
('F#:minor', 9),
('G:mixolydian', 0),
('Db', 1),
('F:dorian', 3),
('D:aeolian', 5),
('A:mixolydian', 2),
('C#:modal', 1),
('F#', 6),
('D:mixolydian', 7),
('Eb:minor', 6),
('Ab', 8)]
In [20]:
keys['key_diatonic_root'] = keys['key_label'].apply(diatonic_root_for_key_label)
In [21]:
canonic_pitch_class_labels = dict(enumerate(['C', 'Db', 'D', 'Eb', 'E', 'F', 'Gb', 'G', 'Ab', 'A', 'Bb', 'B']))
def label_for_pitch_class(pc):
if pc in canonic_pitch_class_labels:
return canonic_pitch_class_labels[pc]
In [22]:
keys['key_diatonic_root_label'] = keys['key_diatonic_root'].apply(label_for_pitch_class)
In [23]:
diatonic_keys_hist = keys.dropna()['key_diatonic_root_label'].value_counts()
diatonic_keys_hist
Out[23]:
C 55
A 53
G 52
E 39
D 26
F 23
Eb 15
B 6
Ab 6
Bb 5
Gb 2
Db 2
dtype: int64
In [24]:
plot(diatonic_keys_hist)
xticks(np.arange(len(diatonic_keys_hist)), diatonic_keys_hist.index)
title('diatonic key usage among all songs');
In [25]:
# all tracks start at 0.0 time
assert (keys.groupby('track_id').first()['start'] == 0).all()
# duration of each segment should be positive
# assert (keys.groupby('track_id')['duration'] < 0).all()
In [26]:
keys.groupby('track_id').last()['end']
Out[26]:
track_id
0 175.800
1 110.160
2 177.573
3 146.493
4 147.507
5 147.760
6 123.373
7 142.760
8 125.733
9 158.067
10 119.333
11 125.133
12 112.867
13 153.267
14 133.733
...
165 96.867
166 139.827
167 23.267
168 216.667
169 234.800
170 228.560
171 145.733
172 50.000
173 243.307
174 40.600
175 217.960
176 175.507
177 217.867
178 152.733
179 187.093
Name: end, Length: 180, dtype: float64
In [27]:
keys
Out[27]:
start
end
key_indicator
key_label
track_id
duration
key_diatonic_root
key_diatonic_root_label
0
0.000
1.035
Silence
NaN
0
1.035
NaN
None
1
1.035
171.880
Key
E
0
170.845
4
E
2
171.880
175.800
Silence
NaN
0
3.920
NaN
None
0
0.000
0.395
Silence
NaN
1
0.395
NaN
None
1
0.395
105.685
Key
C
1
105.290
0
C
2
105.685
110.160
Silence
NaN
1
4.475
NaN
None
0
0.000
0.410
Silence
NaN
2
0.410
NaN
None
1
0.410
174.120
Key
D
2
173.710
2
D
2
174.120
177.573
Silence
NaN
2
3.453
NaN
None
0
0.000
146.493
Key
Bb
3
146.493
10
Bb
0
0.000
147.507
Key
E
4
147.507
4
E
0
0.000
147.760
Key
E
5
147.760
4
E
0
0.000
123.373
Key
E
6
123.373
4
E
0
0.000
142.760
Key
G
7
142.760
7
G
0
0.000
125.733
Key
D:aeolian
8
125.733
5
F
0
0.000
158.067
Key
G
9
158.067
7
G
0
0.000
119.333
Key
E
10
119.333
4
E
0
0.000
125.133
Key
F#:minor
11
125.133
9
A
0
0.000
112.867
Key
E
12
112.867
4
E
0
0.000
153.267
Key
D
13
153.267
2
D
0
0.000
1.030
Silence
NaN
14
1.030
NaN
None
1
1.030
129.940
Key
E
14
128.910
4
E
2
129.940
133.733
Silence
NaN
14
3.793
NaN
None
0
0.000
0.429
Silence
NaN
15
0.429
NaN
None
1
0.429
120.271
Key
E
15
119.842
4
E
2
120.271
124.533
Silence
NaN
15
4.262
NaN
None
0
0.000
0.383
Silence
NaN
16
0.383
NaN
None
1
0.383
125.530
Key
E
16
125.147
4
E
2
125.530
129.560
Silence
NaN
16
4.030
NaN
None
0
0.000
0.430
Silence
NaN
17
0.430
NaN
None
...
...
...
...
...
...
...
...
...
2
229.350
234.800
Silence
NaN
169
5.450
NaN
None
0
0.000
0.355
Silence
NaN
170
0.355
NaN
None
1
0.355
222.650
Key
D
170
222.295
2
D
2
222.650
228.560
Silence
NaN
170
5.910
NaN
None
0
0.000
0.375
Silence
NaN
171
0.375
NaN
None
1
0.375
144.340
Key
A:minor
171
143.965
0
C
2
144.340
145.733
Silence
NaN
171
1.393
NaN
None
0
0.000
1.480
Silence
NaN
172
1.480
NaN
None
1
1.480
50.000
Key
F
172
48.520
5
F
2
50.000
50.000
Silence
NaN
172
0.000
NaN
None
0
0.000
241.355
Key
C
173
241.355
0
C
1
241.355
243.307
Silence
NaN
173
1.952
NaN
None
0
0.000
0.345
Silence
NaN
174
0.345
NaN
None
1
0.345
38.005
Key
G
174
37.660
7
G
2
38.005
40.600
Silence
NaN
174
2.595
NaN
None
0
0.000
0.395
Silence
NaN
175
0.395
NaN
None
1
0.395
214.135
Key
A
175
213.740
9
A
2
214.135
217.960
Silence
NaN
175
3.825
NaN
None
0
0.000
0.350
Silence
NaN
176
0.350
NaN
None
1
0.350
170.535
Key
B
176
170.185
11
B
2
170.535
175.507
Silence
NaN
176
4.972
NaN
None
0
0.000
0.345
Silence
NaN
177
0.345
NaN
None
1
0.345
215.990
Key
Eb
177
215.645
3
Eb
2
215.990
217.867
Silence
NaN
177
1.877
NaN
None
0
0.000
0.450
Silence
NaN
178
0.450
NaN
None
1
0.450
149.205
Key
D
178
148.755
2
D
2
149.205
152.733
Silence
NaN
178
3.528
NaN
None
0
0.000
0.365
Silence
NaN
179
0.365
NaN
None
1
0.365
185.355
Key
A:mixolydian
179
184.990
2
D
2
185.355
187.093
Silence
NaN
179
1.738
NaN
None
408 rows × 8 columns
Export the prepared DataFrame to TSV file.
In [28]:
keys = keys[[
'track_id', 'start', 'end', 'duration', 'key_indicator',
'key_label', 'key_diatonic_root_label', 'key_diatonic_root']]
In [29]:
keys.to_csv('data/beatles/derived/all_keys.tsv', sep='\t', index=False, float_format='%.3f')
In [30]:
files = glob.glob('data/beatles/chordlab/The_Beatles/*/*.lab.pcs.tsv')
print('chord files count:', len(files))
files
chord files count: 180
Out[30]:
['data/beatles/chordlab/The_Beatles/01_-_Please_Please_Me/01_-_I_Saw_Her_Standing_There.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/01_-_Please_Please_Me/02_-_Misery.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/01_-_Please_Please_Me/03_-_Anna_(Go_To_Him).lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/01_-_Please_Please_Me/04_-_Chains.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/01_-_Please_Please_Me/05_-_Boys.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/01_-_Please_Please_Me/06_-_Ask_Me_Why.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/01_-_Please_Please_Me/07_-_Please_Please_Me.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/01_-_Please_Please_Me/08_-_Love_Me_Do.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/01_-_Please_Please_Me/09_-_P._S._I_Love_You.lab.pcs.tsv',
"data/beatles/chordlab/The_Beatles/01_-_Please_Please_Me/10_-_Baby_It's_You.lab.pcs.tsv",
'data/beatles/chordlab/The_Beatles/01_-_Please_Please_Me/11_-_Do_You_Want_To_Know_A_Secret.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/01_-_Please_Please_Me/12_-_A_Taste_Of_Honey.lab.pcs.tsv',
"data/beatles/chordlab/The_Beatles/01_-_Please_Please_Me/13_-_There's_A_Place.lab.pcs.tsv",
'data/beatles/chordlab/The_Beatles/01_-_Please_Please_Me/14_-_Twist_And_Shout.lab.pcs.tsv',
"data/beatles/chordlab/The_Beatles/02_-_With_the_Beatles/01_-_It_Won't_Be_Long.lab.pcs.tsv",
"data/beatles/chordlab/The_Beatles/02_-_With_the_Beatles/02_-_All_I've_Got_To_Do.lab.pcs.tsv",
'data/beatles/chordlab/The_Beatles/02_-_With_the_Beatles/03_-_All_My_Loving.lab.pcs.tsv',
"data/beatles/chordlab/The_Beatles/02_-_With_the_Beatles/04_-_Don't_Bother_Me.lab.pcs.tsv",
'data/beatles/chordlab/The_Beatles/02_-_With_the_Beatles/05_-_Little_Child.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/02_-_With_the_Beatles/06_-_Till_There_Was_You.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/02_-_With_the_Beatles/07_-_Please_Mister_Postman.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/02_-_With_the_Beatles/08_-_Roll_Over_Beethoven.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/02_-_With_the_Beatles/09_-_Hold_Me_Tight.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/02_-_With_the_Beatles/10_-_You_Really_Got_A_Hold_On_Me.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/02_-_With_the_Beatles/11_-_I_Wanna_Be_Your_Man.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/02_-_With_the_Beatles/12_-_Devil_In_Her_Heart.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/02_-_With_the_Beatles/13_-_Not_A_Second_Time.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/02_-_With_the_Beatles/14_-_Money.lab.pcs.tsv',
"data/beatles/chordlab/The_Beatles/03_-_A_Hard_Day's_Night/01_-_A_Hard_Day's_Night.lab.pcs.tsv",
"data/beatles/chordlab/The_Beatles/03_-_A_Hard_Day's_Night/02_-_I_Should_Have_Known_Better.lab.pcs.tsv",
"data/beatles/chordlab/The_Beatles/03_-_A_Hard_Day's_Night/03_-_If_I_Fell.lab.pcs.tsv",
"data/beatles/chordlab/The_Beatles/03_-_A_Hard_Day's_Night/04_-_I'm_Happy_Just_To_Dance_With_You.lab.pcs.tsv",
"data/beatles/chordlab/The_Beatles/03_-_A_Hard_Day's_Night/05_-_And_I_Love_Her.lab.pcs.tsv",
"data/beatles/chordlab/The_Beatles/03_-_A_Hard_Day's_Night/06_-_Tell_Me_Why.lab.pcs.tsv",
"data/beatles/chordlab/The_Beatles/03_-_A_Hard_Day's_Night/07_-_Can't_Buy_Me_Love.lab.pcs.tsv",
"data/beatles/chordlab/The_Beatles/03_-_A_Hard_Day's_Night/08_-_Any_Time_At_All.lab.pcs.tsv",
"data/beatles/chordlab/The_Beatles/03_-_A_Hard_Day's_Night/09_-_I'll_Cry_Instead.lab.pcs.tsv",
"data/beatles/chordlab/The_Beatles/03_-_A_Hard_Day's_Night/10_-_Things_We_Said_Today.lab.pcs.tsv",
"data/beatles/chordlab/The_Beatles/03_-_A_Hard_Day's_Night/11_-_When_I_Get_Home.lab.pcs.tsv",
"data/beatles/chordlab/The_Beatles/03_-_A_Hard_Day's_Night/12_-_You_Can't_Do_That.lab.pcs.tsv",
"data/beatles/chordlab/The_Beatles/03_-_A_Hard_Day's_Night/13_-_I'll_Be_Back.lab.pcs.tsv",
'data/beatles/chordlab/The_Beatles/04_-_Beatles_for_Sale/01_-_No_Reply.lab.pcs.tsv',
"data/beatles/chordlab/The_Beatles/04_-_Beatles_for_Sale/02_-_I'm_a_Loser.lab.pcs.tsv",
"data/beatles/chordlab/The_Beatles/04_-_Beatles_for_Sale/03_-_Baby's_In_Black.lab.pcs.tsv",
'data/beatles/chordlab/The_Beatles/04_-_Beatles_for_Sale/04_-_Rock_and_Roll_Music.lab.pcs.tsv',
"data/beatles/chordlab/The_Beatles/04_-_Beatles_for_Sale/05_-_I'll_Follow_the_Sun.lab.pcs.tsv",
'data/beatles/chordlab/The_Beatles/04_-_Beatles_for_Sale/06_-_Mr._Moonlight.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/04_-_Beatles_for_Sale/07_-_Kansas_City-_Hey,_Hey,_Hey,_Hey.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/04_-_Beatles_for_Sale/08_-_Eight_Days_a_Week.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/04_-_Beatles_for_Sale/09_-_Words_of_Love.lab.pcs.tsv',
"data/beatles/chordlab/The_Beatles/04_-_Beatles_for_Sale/10_-_Honey_Don't.lab.pcs.tsv",
'data/beatles/chordlab/The_Beatles/04_-_Beatles_for_Sale/11_-_Every_Little_Thing.lab.pcs.tsv',
"data/beatles/chordlab/The_Beatles/04_-_Beatles_for_Sale/12_-_I_Don't_Want_to_Spoil_the_Party.lab.pcs.tsv",
"data/beatles/chordlab/The_Beatles/04_-_Beatles_for_Sale/13_-_What_You're_Doing.lab.pcs.tsv",
"data/beatles/chordlab/The_Beatles/04_-_Beatles_for_Sale/14_-_Everybody's_Trying_to_Be_My_Baby.lab.pcs.tsv",
'data/beatles/chordlab/The_Beatles/05_-_Help!/01_-_Help!.lab.pcs.tsv',
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'data/beatles/chordlab/The_Beatles/12_-_Let_It_Be/04_-_I_Me_Mine.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/12_-_Let_It_Be/05_-_Dig_It.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/12_-_Let_It_Be/06_-_Let_It_Be.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/12_-_Let_It_Be/07_-_Maggie_Mae.lab.pcs.tsv',
"data/beatles/chordlab/The_Beatles/12_-_Let_It_Be/08_-_I've_Got_A_Feeling.lab.pcs.tsv",
'data/beatles/chordlab/The_Beatles/12_-_Let_It_Be/09_-_One_After_909.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/12_-_Let_It_Be/10_-_The_Long_and_Winding_Road.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/12_-_Let_It_Be/11_-_For_You_Blue.lab.pcs.tsv',
'data/beatles/chordlab/The_Beatles/12_-_Let_It_Be/12_-_Get_Back.lab.pcs.tsv']
In [31]:
def read_chord_file(path):
return pd.read_csv(path, sep=' ', header=None, names=['start','end','chord_label'])
def read_chord_file_with_pitch_classes(path):
return pd.read_csv(path, sep='\t')
In [32]:
read_chord_file_with_pitch_classes(files[0])
Out[32]:
start
end
label
root
bass
C
Db
D
Eb
E
F
Gb
G
Ab
A
Bb
B
0
0.000000
2.612267
N
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
2.612267
11.459070
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
2
11.459070
12.921927
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
3
12.921927
17.443474
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
4
17.443474
20.410362
B
11
11
0
0
0
1
0
0
1
0
0
0
0
1
5
20.410362
21.908049
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
6
21.908049
23.370907
E:7/3
4
8
0
0
1
0
1
0
0
0
1
0
0
1
7
23.370907
24.856984
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
8
24.856984
26.343061
A:min/b3
9
0
1
0
0
0
1
0
0
0
0
1
0
0
9
26.343061
27.840748
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
10
27.840748
29.350045
B
11
11
0
0
0
1
0
0
1
0
0
0
0
1
11
29.350045
35.305963
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
12
35.305963
36.803650
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
13
36.803650
41.263102
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
14
41.263102
44.245646
B
11
11
0
0
0
1
0
0
1
0
0
0
0
1
15
44.245646
45.720113
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
16
45.720113
47.206190
E:7/3
4
8
0
0
1
0
1
0
0
0
1
0
0
1
17
47.206190
48.692267
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
18
48.692267
50.155124
A:min/b3
9
0
1
0
0
0
1
0
0
0
0
1
0
0
19
50.155124
51.652811
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
20
51.652811
53.138888
B
11
11
0
0
0
1
0
0
1
0
0
0
0
1
21
53.138888
56.111043
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
22
56.111043
65.131995
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
23
65.131995
68.150589
B
11
11
0
0
0
1
0
0
1
0
0
0
0
1
24
68.150589
71.192403
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
25
71.192403
74.199387
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
26
74.199387
75.697074
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
27
75.697074
80.236575
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
28
80.236575
83.208730
B
11
11
0
0
0
1
0
0
1
0
0
0
0
1
29
83.208730
86.221693
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
32
89.257528
90.720385
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
33
90.720385
92.157453
B
11
11
0
0
0
1
0
0
1
0
0
0
0
1
34
92.157453
104.106689
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
35
104.106689
107.125283
B
11
11
0
0
0
1
0
0
1
0
0
0
0
1
36
107.125283
110.178707
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
37
110.178707
113.124087
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
38
113.124087
114.613718
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
39
114.613718
116.099795
B
11
11
0
0
0
1
0
0
1
0
0
0
0
1
40
116.099795
118.944961
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
41
118.944961
128.046462
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
42
128.046462
131.053446
B
11
11
0
0
0
1
0
0
1
0
0
0
0
1
43
131.053446
134.037210
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
44
134.037210
137.044195
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
45
137.044195
138.475524
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
46
138.475524
143.058163
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
47
143.058163
146.041927
B
11
11
0
0
0
1
0
0
1
0
0
0
0
1
48
146.041927
147.551224
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
49
147.551224
149.060521
E:7/3
4
8
0
0
1
0
1
0
0
0
1
0
0
1
50
149.060521
150.511768
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
51
150.511768
152.021065
A:min/b3
9
0
1
0
0
0
1
0
0
0
0
1
0
0
52
152.021065
153.530362
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
53
153.530362
155.062879
B
11
11
0
0
0
1
0
0
1
0
0
0
0
1
54
155.062879
159.532721
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
55
159.532721
161.065238
B
11
11
0
0
0
1
0
0
1
0
0
0
0
1
56
161.065238
165.581519
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
57
165.581519
167.114036
B
11
11
0
0
0
1
0
0
1
0
0
0
0
1
58
167.114036
168.646553
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
59
168.646553
169.737409
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
60
169.737409
171.687173
E:9
4
4
0
0
1
0
1
0
1
0
1
0
0
1
61
171.687173
175.804082
N
0
0
0
0
0
0
0
0
0
0
0
0
0
0
62 rows × 17 columns
In [33]:
chords = pd.concat(add_track_id(read_chord_file_with_pitch_classes(file), track_id) for (track_id, file) in enumerate(files))
In [34]:
chords['duration'] = chords['end'] - chords['start']
In [35]:
chords = chords.reindex_axis(['track_id', 'start', 'end', 'duration', 'label', 'root', 'bass', 'C','Db','D','Eb','E','F','Gb','G','Ab','A','Bb','B'], axis=1)
chords
Out[35]:
track_id
start
end
duration
label
root
bass
C
Db
D
Eb
E
F
Gb
G
Ab
A
Bb
B
0
0
0.000000
2.612267
2.612267
N
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
2.612267
11.459070
8.846803
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
2
0
11.459070
12.921927
1.462857
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
3
0
12.921927
17.443474
4.521547
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
4
0
17.443474
20.410362
2.966888
B
11
11
0
0
0
1
0
0
1
0
0
0
0
1
5
0
20.410362
21.908049
1.497687
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
6
0
21.908049
23.370907
1.462858
E:7/3
4
8
0
0
1
0
1
0
0
0
1
0
0
1
7
0
23.370907
24.856984
1.486077
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
8
0
24.856984
26.343061
1.486077
A:min/b3
9
0
1
0
0
0
1
0
0
0
0
1
0
0
9
0
26.343061
27.840748
1.497687
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
10
0
27.840748
29.350045
1.509297
B
11
11
0
0
0
1
0
0
1
0
0
0
0
1
11
0
29.350045
35.305963
5.955918
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
12
0
35.305963
36.803650
1.497687
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
13
0
36.803650
41.263102
4.459452
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
14
0
41.263102
44.245646
2.982544
B
11
11
0
0
0
1
0
0
1
0
0
0
0
1
15
0
44.245646
45.720113
1.474467
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
16
0
45.720113
47.206190
1.486077
E:7/3
4
8
0
0
1
0
1
0
0
0
1
0
0
1
17
0
47.206190
48.692267
1.486077
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
18
0
48.692267
50.155124
1.462857
A:min/b3
9
0
1
0
0
0
1
0
0
0
0
1
0
0
19
0
50.155124
51.652811
1.497687
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
20
0
51.652811
53.138888
1.486077
B
11
11
0
0
0
1
0
0
1
0
0
0
0
1
21
0
53.138888
56.111043
2.972155
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
22
0
56.111043
65.131995
9.020952
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
23
0
65.131995
68.150589
3.018594
B
11
11
0
0
0
1
0
0
1
0
0
0
0
1
24
0
68.150589
71.192403
3.041814
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
25
0
71.192403
74.199387
3.006984
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
26
0
74.199387
75.697074
1.497687
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
27
0
75.697074
80.236575
4.539501
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
28
0
80.236575
83.208730
2.972155
B
11
11
0
0
0
1
0
0
1
0
0
0
0
1
29
0
83.208730
86.221693
3.012963
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
54
179
120.053537
122.004013
1.950476
D
2
2
0
0
1
0
0
0
1
0
0
1
0
0
55
179
122.004013
122.967641
0.963628
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
56
179
122.967641
123.942879
0.975238
G/2
7
9
0
0
1
0
0
0
0
1
0
1
0
1
57
179
123.942879
127.843832
3.900953
A:7
9
9
0
1
0
0
1
0
0
1
0
1
0
0
58
179
127.843832
129.805918
1.962086
D
2
2
0
0
1
0
0
0
1
0
0
1
0
0
59
179
129.805918
130.792766
0.986848
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
60
179
130.792766
131.245555
0.452789
G/2
7
9
0
0
1
0
0
0
0
1
0
1
0
1
61
179
131.245555
131.756394
0.510839
D/5
2
9
0
0
1
0
0
0
1
0
0
1
0
0
62
179
131.756394
135.622517
3.866123
A:7
9
9
0
1
0
0
1
0
0
1
0
1
0
0
63
179
135.622517
137.549773
1.927256
D
2
2
0
0
1
0
0
0
1
0
0
1
0
0
64
179
137.549773
139.477029
1.927256
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
65
179
139.477029
143.308321
3.831292
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
66
179
143.308321
145.258798
1.950477
D
2
2
0
0
1
0
0
0
1
0
0
1
0
0
67
179
145.258798
146.245646
0.986848
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
68
179
146.245646
146.733265
0.487619
G/2
7
9
0
0
1
0
0
0
0
1
0
1
0
1
69
179
146.733265
147.244104
0.510839
D/5
2
9
0
0
1
0
0
0
1
0
0
1
0
0
70
179
147.244104
151.110226
3.866122
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
71
179
151.110226
153.060702
1.950476
D
2
2
0
0
1
0
0
0
1
0
0
1
0
0
72
179
153.060702
154.024331
0.963629
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
73
179
154.024331
154.523560
0.499229
G/2
7
9
0
0
1
0
0
0
0
1
0
1
0
1
74
179
154.523560
155.022789
0.499229
D/5
2
9
0
0
1
0
0
0
1
0
0
1
0
0
75
179
155.022789
158.854081
3.831292
A:7
9
9
0
1
0
0
1
0
0
1
0
1
0
0
76
179
158.854081
160.781337
1.927256
D
2
2
0
0
1
0
0
0
1
0
0
1
0
0
77
179
160.781337
161.489546
0.708209
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
78
179
161.489546
162.174535
0.684989
G/2
7
9
0
0
1
0
0
0
0
1
0
1
0
1
79
179
162.174535
162.696984
0.522449
D/5
2
9
0
0
1
0
0
0
1
0
0
1
0
0
80
179
162.696984
166.423786
3.726802
A:7
9
9
0
1
0
0
1
0
0
1
0
1
0
0
81
179
166.423786
168.192617
1.768831
D
2
2
0
0
1
0
0
0
1
0
0
1
0
0
82
179
168.192617
174.281557
6.088940
D:7
2
2
1
0
1
0
0
0
1
0
0
1
0
0
83
179
174.281557
186.984490
12.702933
N
0
0
0
0
0
0
0
0
0
0
0
0
0
0
14621 rows × 19 columns
In [36]:
# all tracks start at 0.0 time
assert (chords.groupby('track_id').first()['start'] == 0).all()
In [37]:
chords_keys_end_diff = chords.groupby('track_id').last()[['end']].rename(columns={'end': 'chords_end'}).join(keys.groupby('track_id').last()[['end']].rename(columns={'end': 'keys_end'}))
chords_keys_end_diff['diff'] = chords_keys_end_diff['chords_end'] - chords_keys_end_diff['keys_end']
In [38]:
chords_keys_end_diff['diff'].describe()
Out[38]:
count 180.000000
mean 0.005191
std 0.035198
min -0.133061
25% 0.005714
50% 0.014663
75% 0.020469
max 0.039245
Name: diff, dtype: float64
Track lengths are not precisely aligned between chords and keys dataset. Roughly they're are alrigth, however.
In [39]:
chords_keys_end_diff
Out[39]:
chords_end
keys_end
diff
track_id
0
175.804082
175.800
0.004082
1
110.184490
110.160
0.024490
2
177.580408
177.573
0.007408
3
146.494694
146.493
0.001694
4
147.513469
147.507
0.006469
5
147.774694
147.760
0.014694
6
123.376327
123.373
0.003327
7
142.759184
142.760
-0.000816
8
125.753469
125.733
0.020469
9
158.066939
158.067
-0.000061
10
119.353469
119.333
0.020469
11
125.152653
125.133
0.019653
12
112.875102
112.867
0.008102
13
153.155918
153.267
-0.111082
14
133.746939
133.733
0.013939
15
124.551837
124.533
0.018837
16
129.567347
129.560
0.007347
17
149.394286
149.373
0.021286
18
108.016327
108.000
0.016327
19
136.751020
136.733
0.018020
20
156.734694
156.733
0.001694
21
167.627755
167.627
0.000755
22
152.581224
152.573
0.008224
23
182.909388
182.893
0.016388
24
118.987755
118.973
0.014755
25
147.670204
147.667
0.003204
26
128.339592
128.333
0.006592
27
167.444898
167.560
-0.115102
28
152.555102
152.520
0.035102
29
164.101224
164.080
0.021224
...
...
...
...
150
191.686531
191.800
-0.113469
151
260.649796
260.627
0.022796
152
183.013878
183.000
0.013878
153
207.281633
207.267
0.014633
154
206.706939
206.707
-0.000061
155
171.180408
171.160
0.020408
156
467.173878
467.173
0.000878
157
185.573878
185.560
0.013878
158
165.537959
165.533
0.004959
159
242.416327
242.400
0.016327
160
146.311837
146.307
0.004837
161
66.298776
66.293
0.005776
162
72.751020
72.733
0.018020
163
117.733878
117.733
0.000878
164
91.585306
91.573
0.012306
165
96.888163
96.867
0.021163
166
139.833469
139.827
0.006469
167
23.144490
23.267
-0.122510
168
216.685714
216.667
0.018714
169
234.814694
234.800
0.014694
170
228.571429
228.560
0.011429
171
145.737143
145.733
0.004143
172
50.024490
50.000
0.024490
173
243.330612
243.307
0.023612
174
40.620408
40.600
0.020408
175
217.965714
217.960
0.005714
176
175.516735
175.507
0.009735
177
217.887347
217.867
0.020347
178
152.737959
152.733
0.004959
179
186.984490
187.093
-0.108510
180 rows × 3 columns
In [40]:
chords_keys_end_diff['diff'].hist(bins=50);
In [41]:
chords.head()
Out[41]:
track_id
start
end
duration
label
root
bass
C
Db
D
Eb
E
F
Gb
G
Ab
A
Bb
B
0
0
0.000000
2.612267
2.612267
N
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
2.612267
11.459070
8.846803
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
2
0
11.459070
12.921927
1.462857
A
9
9
0
1
0
0
1
0
0
0
0
1
0
0
3
0
12.921927
17.443474
4.521547
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
4
0
17.443474
20.410362
2.966888
B
11
11
0
0
0
1
0
0
1
0
0
0
0
1
In [42]:
keys.head()
Out[42]:
track_id
start
end
duration
key_indicator
key_label
key_diatonic_root_label
key_diatonic_root
0
0
0.000
1.035
1.035
Silence
NaN
None
NaN
1
0
1.035
171.880
170.845
Key
E
E
4
2
0
171.880
175.800
3.920
Silence
NaN
None
NaN
0
1
0.000
0.395
0.395
Silence
NaN
None
NaN
1
1
0.395
105.685
105.290
Key
C
C
0
Let's try to merge keys to chords from a single example track.
In [43]:
track_keys = keys[keys['track_id'] == 109]
track_keys
Out[43]:
track_id
start
end
duration
key_indicator
key_label
key_diatonic_root_label
key_diatonic_root
0
109
0.000
135.830
135.830
Key
G
G
7
1
109
135.830
198.310
62.480
Key
E
E
4
2
109
198.310
259.226
60.916
Key
G
G
7
3
109
259.226
333.907
74.681
Key
E
E
4
4
109
333.907
333.907
0.000
Silence
NaN
None
NaN
In [44]:
track_chords = chords[chords['track_id'] == 109]
track_chords
Out[44]:
track_id
start
end
duration
label
root
bass
C
Db
D
Eb
E
F
Gb
G
Ab
A
Bb
B
0
109
0.000000
1.914761
1.914761
G
7
7
0
0
1
0
0
0
0
1
0
0
0
1
1
109
1.914761
3.369980
1.455219
B:min
11
11
0
0
1
0
0
0
1
0
0
0
0
1
2
109
3.369980
6.519569
3.149589
E:min7
4
4
0
0
1
0
1
0
0
1
0
0
0
1
3
109
6.519569
12.848047
6.328478
C
0
0
1
0
0
0
1
0
0
1
0
0
0
0
4
109
12.848047
14.425963
1.577916
G
7
7
0
0
1
0
0
0
0
1
0
0
0
1
5
109
14.425963
15.923650
1.497687
B:min/5
11
6
0
0
1
0
0
0
1
0
0
0
0
1
6
109
15.923650
17.490997
1.567347
E:min7
4
4
0
0
1
0
1
0
0
1
0
0
0
1
7
109
17.490997
18.988684
1.497687
E:min7/b7
4
2
0
0
1
0
1
0
0
1
0
0
0
1
8
109
18.988684
20.556031
1.567347
C
0
0
1
0
0
0
1
0
0
1
0
0
0
0
9
109
20.556031
22.123378
1.567347
E:min/5
4
11
0
0
0
0
1
0
0
1
0
0
0
1
10
109
22.123378
25.223242
3.099864
A:min
9
9
1
0
0
0
1
0
0
0
0
1
0
0
11
109
25.223242
26.744149
1.520907
G
7
7
0
0
1
0
0
0
0
1
0
0
0
1
12
109
26.744149
28.369546
1.625397
B:min
11
11
0
0
1
0
0
0
1
0
0
0
0
1
13
109
28.369546
29.925283
1.555737
E:min7
4
4
0
0
1
0
1
0
0
1
0
0
0
1
14
109
29.925283
31.446190
1.520907
E:min7/b7
4
2
0
0
1
0
1
0
0
1
0
0
0
1
15
109
31.446190
33.059977
1.613787
C
0
0
1
0
0
0
1
0
0
1
0
0
0
0
16
109
33.059977
34.592494
1.532517
F
5
5
1
0
0
0
0
1
0
0
0
1
0
0
17
109
34.592494
36.148231
1.555737
E:min
4
4
0
0
0
0
1
0
0
1
0
0
0
1
18
109
36.148231
37.680748
1.532517
E:min7/b7
4
2
0
0
1
0
1
0
0
1
0
0
0
1
19
109
37.680748
39.294535
1.613787
C
0
0
1
0
0
0
1
0
0
1
0
0
0
0
20
109
39.294535
40.815442
1.520907
F
5
5
1
0
0
0
0
1
0
0
0
1
0
0
21
109
40.815442
42.382789
1.567347
E:min
4
4
0
0
0
0
1
0
0
1
0
0
0
1
22
109
42.382789
43.543786
1.160997
C
0
0
1
0
0
0
1
0
0
1
0
0
0
0
23
109
43.543786
43.950136
0.406350
D
2
2
0
0
1
0
0
0
1
0
0
1
0
0
24
109
43.950136
45.517482
1.567346
G
7
7
0
0
1
0
0
0
0
1
0
0
0
1
25
109
45.517482
47.003560
1.486078
B:min/5
11
6
0
0
1
0
0
0
1
0
0
0
0
1
26
109
47.003560
50.033764
3.030204
E:min7/b7
4
2
0
0
1
0
1
0
0
1
0
0
0
1
27
109
50.033764
51.659160
1.625396
C
0
0
1
0
0
0
1
0
0
1
0
0
0
0
28
109
51.659160
53.145238
1.486078
E:min/5
4
11
0
0
0
0
1
0
0
1
0
0
0
1
29
109
53.145238
56.349591
3.204353
A:min
9
9
1
0
0
0
1
0
0
0
0
1
0
0
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
90
109
198.339614
199.872131
1.532517
G
7
7
0
0
1
0
0
0
0
1
0
0
0
1
91
109
199.872131
201.323378
1.451247
B:min/5
11
6
0
0
1
0
0
0
1
0
0
0
0
1
92
109
201.323378
202.855895
1.532517
E:min7
4
4
0
0
1
0
1
0
0
1
0
0
0
1
93
109
202.855895
204.318752
1.462857
E:min7/b7
4
2
0
0
1
0
1
0
0
1
0
0
0
1
94
109
204.318752
205.781609
1.462857
C
0
0
1
0
0
0
1
0
0
1
0
0
0
0
95
109
205.781609
207.209637
1.428028
E:min/5
4
11
0
0
0
0
1
0
0
1
0
0
0
1
96
109
207.209637
209.136893
1.927256
A:min
9
9
1
0
0
0
1
0
0
0
0
1
0
0
97
109
209.136893
210.216621
1.079728
F#:hdim7
6
6
1
0
0
0
1
0
1
0
0
1
0
0
98
109
210.216621
211.656258
1.439637
G
7
7
0
0
1
0
0
0
0
1
0
0
0
1
99
109
211.656258
213.107505
1.451247
B:min
11
11
0
0
1
0
0
0
1
0
0
0
0
1
100
109
213.107505
214.616802
1.509297
E:min7
4
4
0
0
1
0
1
0
0
1
0
0
0
1
101
109
214.616802
216.068049
1.451247
E:min7/b7
4
2
0
0
1
0
1
0
0
1
0
0
0
1
102
109
216.068049
217.554126
1.486077
C
0
0
1
0
0
0
1
0
0
1
0
0
0
0
103
109
217.554126
218.993764
1.439638
F
5
5
1
0
0
0
0
1
0
0
0
1
0
0
104
109
218.993764
220.491451
1.497687
E:min
4
4
0
0
0
0
1
0
0
1
0
0
0
1
105
109
220.491451
221.942698
1.451247
E:min7/b7
4
2
0
0
1
0
1
0
0
1
0
0
0
1
106
109
221.942698
224.856802
2.914104
C
0
0
1
0
0
0
1
0
0
1
0
0
0
0
107
109
224.856802
226.354489
1.497687
C
0
0
1
0
0
0
1
0
0
1
0
0
0
0
108
109
226.354489
227.782517
1.428028
F#:(1,4,b5)
6
6
1
0
0
0
0
0
1
0
0
0
0
1
109
109
227.782517
229.245374
1.462857
E:min/b3
4
7
0
0
0
0
1
0
0
1
0
0
0
1
110
109
229.245374
230.719841
1.474467
A:min(2)
9
9
1
0
0
0
1
0
0
0
0
1
0
1
111
109
230.719841
232.136258
1.416417
E:min(*b3)/5
4
11
0
0
0
0
1
0
0
1
0
0
0
1
112
109
232.136258
233.564285
1.428027
C:maj7(*5)
0
0
1
0
0
0
1
0
0
0
0
0
0
1
113
109
233.564285
234.992312
1.428027
E:min7(*5)/b7
4
2
0
0
1
0
1
0
0
1
0
0
0
0
114
109
234.992312
241.336491
6.344179
E:(1,2,5,b6)
4
4
1
0
0
0
1
0
1
0
0
0
0
1
115
109
241.336491
259.316098
17.979607
N
0
0
0
0
0
0
0
0
0
0
0
0
0
0
116
109
259.316098
261.154269
1.838171
E:(1)
4
4
0
0
0
0
1
0
0
0
0
0
0
0
117
109
261.154269
303.623671
42.469402
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
118
109
303.623671
306.859617
3.235946
N
0
0
0
0
0
0
0
0
0
0
0
0
0
0
119
109
306.859617
333.792653
26.933036
N
0
0
0
0
0
0
0
0
0
0
0
0
0
0
120 rows × 19 columns
In [27]:
def plot_time_intervals(starts, ends, **kwargs):
x_lines = [el for (s, e) in zip(starts, ends) for el in (s, e, None)]
y_lines = [el for i in range(len(starts)) for el in (i, i, None)]
plot(x_lines, y_lines, **kwargs)
def plot_chords_and_keys(track_chords, track_keys):
plot_time_intervals(track_chords['start'], track_chords['end'], label='chord segments')
plot_time_intervals(track_keys['start'], track_keys['end'], label='key segments')
title('chord and key segments in time')
legend(loc='center right')
xlabel('time (sec)')
ylabel('segment index')
In [ ]:
plot_chords_and_keys(track_chords, track_keys);
In [46]:
def time_range(df, start, end):
return df[(df['start'] >= start) & (df['end'] <= end)]
plot_chords_and_keys(time_range(track_chords, 120, 220), time_range(track_keys, 120, 220))
In [47]:
def find_key(keys, track_id, start):
"Finds the first key segment within a track that the chord segment spans."
possible_keys = keys.query('(start <= '+str(start)+') & (track_id == '+str(track_id)+')')
if len(possible_keys) > 0:
row = possible_keys.iloc[-1]
# return pd.Series([start, ['start']])
return row
find_key(keys, 109, 198.0)
Out[47]:
track_id 109
start 135.83
end 198.31
duration 62.48
key_indicator Key
key_label E
key_diatonic_root_label E
key_diatonic_root 4
Name: 1, dtype: object
In [48]:
track_chords['start'].apply(lambda start: find_key(track_keys, 109, start)[['key_diatonic_root_label', 'key_diatonic_root']])
Out[48]:
key_diatonic_root_label
key_diatonic_root
0
G
7
1
G
7
2
G
7
3
G
7
4
G
7
5
G
7
6
G
7
7
G
7
8
G
7
9
G
7
10
G
7
11
G
7
12
G
7
13
G
7
14
G
7
15
G
7
16
G
7
17
G
7
18
G
7
19
G
7
20
G
7
21
G
7
22
G
7
23
G
7
24
G
7
25
G
7
26
G
7
27
G
7
28
G
7
29
G
7
...
...
...
90
G
7
91
G
7
92
G
7
93
G
7
94
G
7
95
G
7
96
G
7
97
G
7
98
G
7
99
G
7
100
G
7
101
G
7
102
G
7
103
G
7
104
G
7
105
G
7
106
G
7
107
G
7
108
G
7
109
G
7
110
G
7
111
G
7
112
G
7
113
G
7
114
G
7
115
G
7
116
E
4
117
E
4
118
E
4
119
E
4
120 rows × 2 columns
In [49]:
track_keys
Out[49]:
track_id
start
end
duration
key_indicator
key_label
key_diatonic_root_label
key_diatonic_root
0
109
0.000
135.830
135.830
Key
G
G
7
1
109
135.830
198.310
62.480
Key
E
E
4
2
109
198.310
259.226
60.916
Key
G
G
7
3
109
259.226
333.907
74.681
Key
E
E
4
4
109
333.907
333.907
0.000
Silence
NaN
None
NaN
In [50]:
track_chords
Out[50]:
track_id
start
end
duration
label
root
bass
C
Db
D
Eb
E
F
Gb
G
Ab
A
Bb
B
0
109
0.000000
1.914761
1.914761
G
7
7
0
0
1
0
0
0
0
1
0
0
0
1
1
109
1.914761
3.369980
1.455219
B:min
11
11
0
0
1
0
0
0
1
0
0
0
0
1
2
109
3.369980
6.519569
3.149589
E:min7
4
4
0
0
1
0
1
0
0
1
0
0
0
1
3
109
6.519569
12.848047
6.328478
C
0
0
1
0
0
0
1
0
0
1
0
0
0
0
4
109
12.848047
14.425963
1.577916
G
7
7
0
0
1
0
0
0
0
1
0
0
0
1
5
109
14.425963
15.923650
1.497687
B:min/5
11
6
0
0
1
0
0
0
1
0
0
0
0
1
6
109
15.923650
17.490997
1.567347
E:min7
4
4
0
0
1
0
1
0
0
1
0
0
0
1
7
109
17.490997
18.988684
1.497687
E:min7/b7
4
2
0
0
1
0
1
0
0
1
0
0
0
1
8
109
18.988684
20.556031
1.567347
C
0
0
1
0
0
0
1
0
0
1
0
0
0
0
9
109
20.556031
22.123378
1.567347
E:min/5
4
11
0
0
0
0
1
0
0
1
0
0
0
1
10
109
22.123378
25.223242
3.099864
A:min
9
9
1
0
0
0
1
0
0
0
0
1
0
0
11
109
25.223242
26.744149
1.520907
G
7
7
0
0
1
0
0
0
0
1
0
0
0
1
12
109
26.744149
28.369546
1.625397
B:min
11
11
0
0
1
0
0
0
1
0
0
0
0
1
13
109
28.369546
29.925283
1.555737
E:min7
4
4
0
0
1
0
1
0
0
1
0
0
0
1
14
109
29.925283
31.446190
1.520907
E:min7/b7
4
2
0
0
1
0
1
0
0
1
0
0
0
1
15
109
31.446190
33.059977
1.613787
C
0
0
1
0
0
0
1
0
0
1
0
0
0
0
16
109
33.059977
34.592494
1.532517
F
5
5
1
0
0
0
0
1
0
0
0
1
0
0
17
109
34.592494
36.148231
1.555737
E:min
4
4
0
0
0
0
1
0
0
1
0
0
0
1
18
109
36.148231
37.680748
1.532517
E:min7/b7
4
2
0
0
1
0
1
0
0
1
0
0
0
1
19
109
37.680748
39.294535
1.613787
C
0
0
1
0
0
0
1
0
0
1
0
0
0
0
20
109
39.294535
40.815442
1.520907
F
5
5
1
0
0
0
0
1
0
0
0
1
0
0
21
109
40.815442
42.382789
1.567347
E:min
4
4
0
0
0
0
1
0
0
1
0
0
0
1
22
109
42.382789
43.543786
1.160997
C
0
0
1
0
0
0
1
0
0
1
0
0
0
0
23
109
43.543786
43.950136
0.406350
D
2
2
0
0
1
0
0
0
1
0
0
1
0
0
24
109
43.950136
45.517482
1.567346
G
7
7
0
0
1
0
0
0
0
1
0
0
0
1
25
109
45.517482
47.003560
1.486078
B:min/5
11
6
0
0
1
0
0
0
1
0
0
0
0
1
26
109
47.003560
50.033764
3.030204
E:min7/b7
4
2
0
0
1
0
1
0
0
1
0
0
0
1
27
109
50.033764
51.659160
1.625396
C
0
0
1
0
0
0
1
0
0
1
0
0
0
0
28
109
51.659160
53.145238
1.486078
E:min/5
4
11
0
0
0
0
1
0
0
1
0
0
0
1
29
109
53.145238
56.349591
3.204353
A:min
9
9
1
0
0
0
1
0
0
0
0
1
0
0
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
90
109
198.339614
199.872131
1.532517
G
7
7
0
0
1
0
0
0
0
1
0
0
0
1
91
109
199.872131
201.323378
1.451247
B:min/5
11
6
0
0
1
0
0
0
1
0
0
0
0
1
92
109
201.323378
202.855895
1.532517
E:min7
4
4
0
0
1
0
1
0
0
1
0
0
0
1
93
109
202.855895
204.318752
1.462857
E:min7/b7
4
2
0
0
1
0
1
0
0
1
0
0
0
1
94
109
204.318752
205.781609
1.462857
C
0
0
1
0
0
0
1
0
0
1
0
0
0
0
95
109
205.781609
207.209637
1.428028
E:min/5
4
11
0
0
0
0
1
0
0
1
0
0
0
1
96
109
207.209637
209.136893
1.927256
A:min
9
9
1
0
0
0
1
0
0
0
0
1
0
0
97
109
209.136893
210.216621
1.079728
F#:hdim7
6
6
1
0
0
0
1
0
1
0
0
1
0
0
98
109
210.216621
211.656258
1.439637
G
7
7
0
0
1
0
0
0
0
1
0
0
0
1
99
109
211.656258
213.107505
1.451247
B:min
11
11
0
0
1
0
0
0
1
0
0
0
0
1
100
109
213.107505
214.616802
1.509297
E:min7
4
4
0
0
1
0
1
0
0
1
0
0
0
1
101
109
214.616802
216.068049
1.451247
E:min7/b7
4
2
0
0
1
0
1
0
0
1
0
0
0
1
102
109
216.068049
217.554126
1.486077
C
0
0
1
0
0
0
1
0
0
1
0
0
0
0
103
109
217.554126
218.993764
1.439638
F
5
5
1
0
0
0
0
1
0
0
0
1
0
0
104
109
218.993764
220.491451
1.497687
E:min
4
4
0
0
0
0
1
0
0
1
0
0
0
1
105
109
220.491451
221.942698
1.451247
E:min7/b7
4
2
0
0
1
0
1
0
0
1
0
0
0
1
106
109
221.942698
224.856802
2.914104
C
0
0
1
0
0
0
1
0
0
1
0
0
0
0
107
109
224.856802
226.354489
1.497687
C
0
0
1
0
0
0
1
0
0
1
0
0
0
0
108
109
226.354489
227.782517
1.428028
F#:(1,4,b5)
6
6
1
0
0
0
0
0
1
0
0
0
0
1
109
109
227.782517
229.245374
1.462857
E:min/b3
4
7
0
0
0
0
1
0
0
1
0
0
0
1
110
109
229.245374
230.719841
1.474467
A:min(2)
9
9
1
0
0
0
1
0
0
0
0
1
0
1
111
109
230.719841
232.136258
1.416417
E:min(*b3)/5
4
11
0
0
0
0
1
0
0
1
0
0
0
1
112
109
232.136258
233.564285
1.428027
C:maj7(*5)
0
0
1
0
0
0
1
0
0
0
0
0
0
1
113
109
233.564285
234.992312
1.428027
E:min7(*5)/b7
4
2
0
0
1
0
1
0
0
1
0
0
0
0
114
109
234.992312
241.336491
6.344179
E:(1,2,5,b6)
4
4
1
0
0
0
1
0
1
0
0
0
0
1
115
109
241.336491
259.316098
17.979607
N
0
0
0
0
0
0
0
0
0
0
0
0
0
0
116
109
259.316098
261.154269
1.838171
E:(1)
4
4
0
0
0
0
1
0
0
0
0
0
0
0
117
109
261.154269
303.623671
42.469402
E
4
4
0
0
0
0
1
0
0
0
1
0
0
1
118
109
303.623671
306.859617
3.235946
N
0
0
0
0
0
0
0
0
0
0
0
0
0
0
119
109
306.859617
333.792653
26.933036
N
0
0
0
0
0
0
0
0
0
0
0
0
0
0
120 rows × 19 columns
In [51]:
chord = chords.iloc[0]
keys_for_track = keys[keys['track_id'] == chord.track_id]
keys_for_track[keys_for_track['start'] >= chord['start']]
Out[51]:
track_id
start
end
duration
key_indicator
key_label
key_diatonic_root_label
key_diatonic_root
0
0
0.000
1.035
1.035
Silence
NaN
None
NaN
1
0
1.035
171.880
170.845
Key
E
E
4
2
0
171.880
175.800
3.920
Silence
NaN
None
NaN
In [52]:
# this is very inefficient, it takes many seconds
# TODO: optimize this!
key_labels_for_chords = chords[['track_id','start']].apply(
lambda row: find_key(keys, row['track_id'], row['start']),
axis=1)[['key_diatonic_root_label', 'key_diatonic_root']]
In [53]:
key_labels_for_chords[:10]
Out[53]:
key_diatonic_root_label
key_diatonic_root
0
None
NaN
1
E
4
2
E
4
3
E
4
4
E
4
5
E
4
6
E
4
7
E
4
8
E
4
9
E
4
Append the computed key for each chord segment.
In [54]:
for col in ('key_diatonic_root_label', 'key_diatonic_root'):
chords[col] = key_labels_for_chords[col]
In [55]:
chords
Out[55]:
track_id
start
end
duration
label
root
bass
C
Db
D
...
E
F
Gb
G
Ab
A
Bb
B
key_diatonic_root_label
key_diatonic_root
0
0
0.000000
2.612267
2.612267
N
0
0
0
0
0
...
0
0
0
0
0
0
0
0
None
NaN
1
0
2.612267
11.459070
8.846803
E
4
4
0
0
0
...
1
0
0
0
1
0
0
1
E
4
2
0
11.459070
12.921927
1.462857
A
9
9
0
1
0
...
1
0
0
0
0
1
0
0
E
4
3
0
12.921927
17.443474
4.521547
E
4
4
0
0
0
...
1
0
0
0
1
0
0
1
E
4
4
0
17.443474
20.410362
2.966888
B
11
11
0
0
0
...
0
0
1
0
0
0
0
1
E
4
5
0
20.410362
21.908049
1.497687
E
4
4
0
0
0
...
1
0
0
0
1
0
0
1
E
4
6
0
21.908049
23.370907
1.462858
E:7/3
4
8
0
0
1
...
1
0
0
0
1
0
0
1
E
4
7
0
23.370907
24.856984
1.486077
A
9
9
0
1
0
...
1
0
0
0
0
1
0
0
E
4
8
0
24.856984
26.343061
1.486077
A:min/b3
9
0
1
0
0
...
1
0
0
0
0
1
0
0
E
4
9
0
26.343061
27.840748
1.497687
E
4
4
0
0
0
...
1
0
0
0
1
0
0
1
E
4
10
0
27.840748
29.350045
1.509297
B
11
11
0
0
0
...
0
0
1
0
0
0
0
1
E
4
11
0
29.350045
35.305963
5.955918
E
4
4
0
0
0
...
1
0
0
0
1
0
0
1
E
4
12
0
35.305963
36.803650
1.497687
A
9
9
0
1
0
...
1
0
0
0
0
1
0
0
E
4
13
0
36.803650
41.263102
4.459452
E
4
4
0
0
0
...
1
0
0
0
1
0
0
1
E
4
14
0
41.263102
44.245646
2.982544
B
11
11
0
0
0
...
0
0
1
0
0
0
0
1
E
4
15
0
44.245646
45.720113
1.474467
E
4
4
0
0
0
...
1
0
0
0
1
0
0
1
E
4
16
0
45.720113
47.206190
1.486077
E:7/3
4
8
0
0
1
...
1
0
0
0
1
0
0
1
E
4
17
0
47.206190
48.692267
1.486077
A
9
9
0
1
0
...
1
0
0
0
0
1
0
0
E
4
18
0
48.692267
50.155124
1.462857
A:min/b3
9
0
1
0
0
...
1
0
0
0
0
1
0
0
E
4
19
0
50.155124
51.652811
1.497687
E
4
4
0
0
0
...
1
0
0
0
1
0
0
1
E
4
20
0
51.652811
53.138888
1.486077
B
11
11
0
0
0
...
0
0
1
0
0
0
0
1
E
4
21
0
53.138888
56.111043
2.972155
E
4
4
0
0
0
...
1
0
0
0
1
0
0
1
E
4
22
0
56.111043
65.131995
9.020952
A
9
9
0
1
0
...
1
0
0
0
0
1
0
0
E
4
23
0
65.131995
68.150589
3.018594
B
11
11
0
0
0
...
0
0
1
0
0
0
0
1
E
4
24
0
68.150589
71.192403
3.041814
A
9
9
0
1
0
...
1
0
0
0
0
1
0
0
E
4
25
0
71.192403
74.199387
3.006984
E
4
4
0
0
0
...
1
0
0
0
1
0
0
1
E
4
26
0
74.199387
75.697074
1.497687
A
9
9
0
1
0
...
1
0
0
0
0
1
0
0
E
4
27
0
75.697074
80.236575
4.539501
E
4
4
0
0
0
...
1
0
0
0
1
0
0
1
E
4
28
0
80.236575
83.208730
2.972155
B
11
11
0
0
0
...
0
0
1
0
0
0
0
1
E
4
29
0
83.208730
86.221693
3.012963
E
4
4
0
0
0
...
1
0
0
0
1
0
0
1
E
4
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
54
179
120.053537
122.004013
1.950476
D
2
2
0
0
1
...
0
0
1
0
0
1
0
0
D
2
55
179
122.004013
122.967641
0.963628
A
9
9
0
1
0
...
1
0
0
0
0
1
0
0
D
2
56
179
122.967641
123.942879
0.975238
G/2
7
9
0
0
1
...
0
0
0
1
0
1
0
1
D
2
57
179
123.942879
127.843832
3.900953
A:7
9
9
0
1
0
...
1
0
0
1
0
1
0
0
D
2
58
179
127.843832
129.805918
1.962086
D
2
2
0
0
1
...
0
0
1
0
0
1
0
0
D
2
59
179
129.805918
130.792766
0.986848
A
9
9
0
1
0
...
1
0
0
0
0
1
0
0
D
2
60
179
130.792766
131.245555
0.452789
G/2
7
9
0
0
1
...
0
0
0
1
0
1
0
1
D
2
61
179
131.245555
131.756394
0.510839
D/5
2
9
0
0
1
...
0
0
1
0
0
1
0
0
D
2
62
179
131.756394
135.622517
3.866123
A:7
9
9
0
1
0
...
1
0
0
1
0
1
0
0
D
2
63
179
135.622517
137.549773
1.927256
D
2
2
0
0
1
...
0
0
1
0
0
1
0
0
D
2
64
179
137.549773
139.477029
1.927256
A
9
9
0
1
0
...
1
0
0
0
0
1
0
0
D
2
65
179
139.477029
143.308321
3.831292
A
9
9
0
1
0
...
1
0
0
0
0
1
0
0
D
2
66
179
143.308321
145.258798
1.950477
D
2
2
0
0
1
...
0
0
1
0
0
1
0
0
D
2
67
179
145.258798
146.245646
0.986848
A
9
9
0
1
0
...
1
0
0
0
0
1
0
0
D
2
68
179
146.245646
146.733265
0.487619
G/2
7
9
0
0
1
...
0
0
0
1
0
1
0
1
D
2
69
179
146.733265
147.244104
0.510839
D/5
2
9
0
0
1
...
0
0
1
0
0
1
0
0
D
2
70
179
147.244104
151.110226
3.866122
A
9
9
0
1
0
...
1
0
0
0
0
1
0
0
D
2
71
179
151.110226
153.060702
1.950476
D
2
2
0
0
1
...
0
0
1
0
0
1
0
0
D
2
72
179
153.060702
154.024331
0.963629
A
9
9
0
1
0
...
1
0
0
0
0
1
0
0
D
2
73
179
154.024331
154.523560
0.499229
G/2
7
9
0
0
1
...
0
0
0
1
0
1
0
1
D
2
74
179
154.523560
155.022789
0.499229
D/5
2
9
0
0
1
...
0
0
1
0
0
1
0
0
D
2
75
179
155.022789
158.854081
3.831292
A:7
9
9
0
1
0
...
1
0
0
1
0
1
0
0
D
2
76
179
158.854081
160.781337
1.927256
D
2
2
0
0
1
...
0
0
1
0
0
1
0
0
D
2
77
179
160.781337
161.489546
0.708209
A
9
9
0
1
0
...
1
0
0
0
0
1
0
0
D
2
78
179
161.489546
162.174535
0.684989
G/2
7
9
0
0
1
...
0
0
0
1
0
1
0
1
D
2
79
179
162.174535
162.696984
0.522449
D/5
2
9
0
0
1
...
0
0
1
0
0
1
0
0
D
2
80
179
162.696984
166.423786
3.726802
A:7
9
9
0
1
0
...
1
0
0
1
0
1
0
0
D
2
81
179
166.423786
168.192617
1.768831
D
2
2
0
0
1
...
0
0
1
0
0
1
0
0
D
2
82
179
168.192617
174.281557
6.088940
D:7
2
2
1
0
1
...
0
0
1
0
0
1
0
0
D
2
83
179
174.281557
186.984490
12.702933
N
0
0
0
0
0
...
0
0
0
0
0
0
0
0
D
2
14621 rows × 21 columns
Key distribution in chord segments.
In [56]:
chords.key_diatonic_root_label.value_counts()
Out[56]:
G 2916
A 2528
E 2221
C 2198
D 1928
F 983
B 491
Eb 446
Bb 274
Gb 184
Ab 182
Db 90
dtype: int64
In [57]:
chords.to_csv('data/beatles/derived/all_chords_with_keys.tsv', sep='\t', index=False, float_format='%.6f')
We can see that the key labels are heavily skewed and this might not be good for our ML models.
In order to deskew the class distribution we can generate more data from the existing data by transposing each data point to all 12 keys. This way we'll have a 12x larger dataset and uniform classes.
In [58]:
chords.head()
Out[58]:
track_id
start
end
duration
label
root
bass
C
Db
D
...
E
F
Gb
G
Ab
A
Bb
B
key_diatonic_root_label
key_diatonic_root
0
0
0.000000
2.612267
2.612267
N
0
0
0
0
0
...
0
0
0
0
0
0
0
0
None
NaN
1
0
2.612267
11.459070
8.846803
E
4
4
0
0
0
...
1
0
0
0
1
0
0
1
E
4
2
0
11.459070
12.921927
1.462857
A
9
9
0
1
0
...
1
0
0
0
0
1
0
0
E
4
3
0
12.921927
17.443474
4.521547
E
4
4
0
0
0
...
1
0
0
0
1
0
0
1
E
4
4
0
17.443474
20.410362
2.966888
B
11
11
0
0
0
...
0
0
1
0
0
0
0
1
E
4
5 rows × 21 columns
In [139]:
def add_pitch_classes(a, b):
return ((a + b) + 12) % 12
def rotate_columns(cols, shift):
return cols[shift:] + cols[:shift]
pcs_columns = ['C','Db','D','Eb','E','F','Gb','G','Ab','A','Bb','B']
[rotate_columns(pcs_columns, i) for i in range(12)]
Out[139]:
[['C', 'Db', 'D', 'Eb', 'E', 'F', 'Gb', 'G', 'Ab', 'A', 'Bb', 'B'],
['Db', 'D', 'Eb', 'E', 'F', 'Gb', 'G', 'Ab', 'A', 'Bb', 'B', 'C'],
['D', 'Eb', 'E', 'F', 'Gb', 'G', 'Ab', 'A', 'Bb', 'B', 'C', 'Db'],
['Eb', 'E', 'F', 'Gb', 'G', 'Ab', 'A', 'Bb', 'B', 'C', 'Db', 'D'],
['E', 'F', 'Gb', 'G', 'Ab', 'A', 'Bb', 'B', 'C', 'Db', 'D', 'Eb'],
['F', 'Gb', 'G', 'Ab', 'A', 'Bb', 'B', 'C', 'Db', 'D', 'Eb', 'E'],
['Gb', 'G', 'Ab', 'A', 'Bb', 'B', 'C', 'Db', 'D', 'Eb', 'E', 'F'],
['G', 'Ab', 'A', 'Bb', 'B', 'C', 'Db', 'D', 'Eb', 'E', 'F', 'Gb'],
['Ab', 'A', 'Bb', 'B', 'C', 'Db', 'D', 'Eb', 'E', 'F', 'Gb', 'G'],
['A', 'Bb', 'B', 'C', 'Db', 'D', 'Eb', 'E', 'F', 'Gb', 'G', 'Ab'],
['Bb', 'B', 'C', 'Db', 'D', 'Eb', 'E', 'F', 'Gb', 'G', 'Ab', 'A'],
['B', 'C', 'Db', 'D', 'Eb', 'E', 'F', 'Gb', 'G', 'Ab', 'A', 'Bb']]
In [84]:
def rotate_binary_pcs_cols(df, cols, shift):
return df.rename_axis(dict(zip(cols, rotate_columns(cols, shift))), axis=1)
rotate_binary_pcs_cols(chords, pcs_columns, 2)[pcs_columns].head()
Out[84]:
C
Db
D
Eb
E
F
Gb
G
Ab
A
Bb
B
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
1
0
0
0
1
0
2
0
0
0
1
0
0
1
0
0
0
0
1
3
0
1
0
0
0
0
1
0
0
0
1
0
4
0
1
0
0
0
1
0
0
1
0
0
0
In [61]:
chords[pcs_columns].head()
Out[61]:
C
Db
D
Eb
E
F
Gb
G
Ab
A
Bb
B
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
1
0
0
1
2
0
1
0
0
1
0
0
0
0
1
0
0
3
0
0
0
0
1
0
0
0
1
0
0
1
4
0
0
0
1
0
0
1
0
0
0
0
1
In [62]:
def transpose_col(df, shift):
return df.apply(lambda pc: add_pitch_classes(pc, shift))
transpose_col(chords[['root', 'bass', 'key_diatonic_root']], 11).head()
Out[62]:
root
bass
key_diatonic_root
0
11
11
NaN
1
3
3
3
2
8
8
3
3
3
3
3
4
10
10
3
In [63]:
def transpose_chords_df(chords, shift):
chords_copy = chords.copy()
chords_copy['synth_transposition'] = shift
pc_cols = ['root', 'bass', 'key_diatonic_root']
chords_copy[pc_cols] = transpose_col(chords_copy[pc_cols], shift)
chords_copy['key_diatonic_root_label'] = chords_copy['key_diatonic_root'].apply(label_for_pitch_class)
chords_copy = rotate_binary_pcs_cols(chords_copy, pcs_columns, shift)
chords_copy = chords_copy.rename_axis({'label': 'orig_chord_label'}, axis=1)
return chords_copy
transpose_chords_df(chords, 1).head()
Out[63]:
track_id
start
end
duration
orig_chord_label
root
bass
Db
D
Eb
...
Gb
G
Ab
A
Bb
B
C
key_diatonic_root_label
key_diatonic_root
synth_transposition
0
0
0.000000
2.612267
2.612267
N
1
1
0
0
0
...
0
0
0
0
0
0
0
None
NaN
1
1
0
2.612267
11.459070
8.846803
E
5
5
0
0
0
...
0
0
0
1
0
0
1
F
5
1
2
0
11.459070
12.921927
1.462857
A
10
10
0
1
0
...
0
0
0
0
1
0
0
F
5
1
3
0
12.921927
17.443474
4.521547
E
5
5
0
0
0
...
0
0
0
1
0
0
1
F
5
1
4
0
17.443474
20.410362
2.966888
B
0
0
0
0
0
...
0
1
0
0
0
0
1
F
5
1
5 rows × 22 columns
In [93]:
def generate_transpositions(chords):
df = pd.concat([transpose_chords_df(chords, shift) for shift in range(12)])
df = df[['track_id', 'synth_transposition',
'start', 'end', 'duration',
'orig_chord_label', 'root', 'bass',
'C', 'Db', 'D', 'Eb', 'E', 'F', 'Gb', 'G', 'Ab', 'A', 'Bb', 'B',
'key_diatonic_root_label', 'key_diatonic_root']]
return df
In [94]:
chords_all_synth = generate_transpositions(chords)
In [95]:
chords_all_synth.columns
Out[95]:
Index(['track_id', 'synth_transposition', 'start', 'end', 'duration', 'orig_chord_label', 'root', 'bass', 'C', 'Db', 'D', 'Eb', 'E', 'F', 'Gb', 'G', 'Ab', 'A', 'Bb', 'B', 'key_diatonic_root_label', 'key_diatonic_root'], dtype='object')
In [96]:
len(chords_all_synth)
Out[96]:
175452
All key classes are now of uniform probability.
In [97]:
chords_all_synth['key_diatonic_root'].value_counts()
Out[97]:
11 14441
10 14441
9 14441
8 14441
7 14441
6 14441
5 14441
4 14441
3 14441
2 14441
1 14441
0 14441
dtype: int64
In [98]:
chords_all_synth[pcs_columns].mean()
Out[98]:
C 0.257164
Db 0.257164
D 0.257164
Eb 0.257164
E 0.257164
F 0.257164
Gb 0.257164
G 0.257164
Ab 0.257164
A 0.257164
Bb 0.257164
B 0.257164
dtype: float64
In [99]:
chords_all_synth.head()
Out[99]:
track_id
synth_transposition
start
end
duration
orig_chord_label
root
bass
C
Db
...
E
F
Gb
G
Ab
A
Bb
B
key_diatonic_root_label
key_diatonic_root
0
0
0
0.000000
2.612267
2.612267
N
0
0
0
0
...
0
0
0
0
0
0
0
0
None
NaN
1
0
0
2.612267
11.459070
8.846803
E
4
4
0
0
...
1
0
0
0
1
0
0
1
E
4
2
0
0
11.459070
12.921927
1.462857
A
9
9
0
1
...
1
0
0
0
0
1
0
0
E
4
3
0
0
12.921927
17.443474
4.521547
E
4
4
0
0
...
1
0
0
0
1
0
0
1
E
4
4
0
0
17.443474
20.410362
2.966888
B
11
11
0
0
...
0
0
1
0
0
0
0
1
E
4
5 rows × 22 columns
In [100]:
chords_all_synth[abs(chords_all_synth['start'] - 2.612267) < 1e-3]
Out[100]:
track_id
synth_transposition
start
end
duration
orig_chord_label
root
bass
C
Db
...
E
F
Gb
G
Ab
A
Bb
B
key_diatonic_root_label
key_diatonic_root
1
0
0
2.612267
11.45907
8.846803
E
4
4
0
0
...
1
0
0
0
1
0
0
1
E
4
1
0
1
2.612267
11.45907
8.846803
E
5
5
1
0
...
0
1
0
0
0
1
0
0
F
5
1
0
2
2.612267
11.45907
8.846803
E
6
6
0
1
...
0
0
1
0
0
0
1
0
Gb
6
1
0
3
2.612267
11.45907
8.846803
E
7
7
0
0
...
0
0
0
1
0
0
0
1
G
7
1
0
4
2.612267
11.45907
8.846803
E
8
8
1
0
...
0
0
0
0
1
0
0
0
Ab
8
1
0
5
2.612267
11.45907
8.846803
E
9
9
0
1
...
1
0
0
0
0
1
0
0
A
9
1
0
6
2.612267
11.45907
8.846803
E
10
10
0
0
...
0
1
0
0
0
0
1
0
Bb
10
1
0
7
2.612267
11.45907
8.846803
E
11
11
0
0
...
0
0
1
0
0
0
0
1
B
11
1
0
8
2.612267
11.45907
8.846803
E
0
0
1
0
...
1
0
0
1
0
0
0
0
C
0
1
0
9
2.612267
11.45907
8.846803
E
1
1
0
1
...
0
1
0
0
1
0
0
0
Db
1
1
0
10
2.612267
11.45907
8.846803
E
2
2
0
0
...
0
0
1
0
0
1
0
0
D
2
1
0
11
2.612267
11.45907
8.846803
E
3
8
0
0
...
0
0
0
1
0
0
1
0
Eb
3
12 rows × 22 columns
In [101]:
chords_all_synth.to_csv('data/beatles/derived/all_chords_with_keys_synth.tsv', sep='\t', index=False, float_format='%.6f')
In [247]:
chords_all = pd.read_csv('data/beatles/derived/all_chords_with_keys.tsv', sep='\t')
chords_all_synth = pd.read_csv('data/beatles/derived/all_chords_with_keys_synth.tsv', sep='\t')
In [248]:
chords_all = chords_all.dropna()
chords_all_synth = chords_all_synth.dropna()
chords_all.head()
Out[248]:
track_id
start
end
duration
label
root
bass
C
Db
D
...
E
F
Gb
G
Ab
A
Bb
B
key_diatonic_root_label
key_diatonic_root
1
0
2.612267
11.459070
8.846803
E
4
4
0
0
0
...
1
0
0
0
1
0
0
1
E
4
2
0
11.459070
12.921927
1.462857
A
9
9
0
1
0
...
1
0
0
0
0
1
0
0
E
4
3
0
12.921927
17.443474
4.521547
E
4
4
0
0
0
...
1
0
0
0
1
0
0
1
E
4
4
0
17.443474
20.410362
2.966888
B
11
11
0
0
0
...
0
0
1
0
0
0
0
1
E
4
5
0
20.410362
21.908049
1.497687
E
4
4
0
0
0
...
1
0
0
0
1
0
0
1
E
4
5 rows × 21 columns
In [98]:
track_chords = chords_all[(chords_all['track_id'] == 40)].copy()
track_chords.head(10)
Out[98]:
track_id
start
end
duration
label
root
bass
C
Db
D
...
F
Gb
G
Ab
A
Bb
B
key_diatonic_root_label
key_diatonic_root
key_index
2976
40
0.000000
0.421247
0.421247
N
0
0
0
0
0
...
0
0
0
0
0
0
0
A
9
53
2977
40
0.421247
1.148857
0.727610
F#:(1,4,b7)
6
6
0
0
0
...
0
1
0
0
0
0
1
A
9
53
2978
40
1.148857
4.997527
3.848670
A
9
9
0
1
0
...
0
0
0
0
1
0
0
A
9
53
2979
40
4.997527
7.053628
2.056101
A:min
9
9
1
0
0
...
0
0
0
0
1
0
0
A
9
53
2980
40
7.053628
8.899614
1.845986
G
7
7
0
0
1
...
0
0
1
0
0
0
1
C
0
54
2981
40
8.899614
10.873310
1.973696
F
5
5
1
0
0
...
1
0
0
0
1
0
0
C
0
54
2982
40
10.873310
12.742517
1.869207
E
4
4
0
0
0
...
0
0
0
1
0
0
1
C
0
54
2983
40
12.742517
16.538979
3.796462
A
9
9
0
1
0
...
0
0
0
0
1
0
0
C
0
54
2984
40
16.538979
18.477845
1.938866
A:min
9
9
1
0
0
...
0
0
0
0
1
0
0
A
9
55
2985
40
18.477845
20.347052
1.869207
G
7
7
0
0
1
...
0
0
1
0
0
0
1
C
0
56
10 rows × 22 columns
In [62]:
chords_all['key_index'] = ((chords_all['key_diatonic_root'].diff() != 0) | (chords_all['track_id'].diff() != 0)).cumsum()
In [63]:
chords_all
Out[63]:
track_id
start
end
duration
label
root
bass
C
Db
D
...
F
Gb
G
Ab
A
Bb
B
key_diatonic_root_label
key_diatonic_root
key_index
1
0
2.612267
11.459070
8.846803
E
4
4
0
0
0
...
0
0
0
1
0
0
1
E
4
1
2
0
11.459070
12.921927
1.462857
A
9
9
0
1
0
...
0
0
0
0
1
0
0
E
4
1
3
0
12.921927
17.443474
4.521547
E
4
4
0
0
0
...
0
0
0
1
0
0
1
E
4
1
4
0
17.443474
20.410362
2.966888
B
11
11
0
0
0
...
0
1
0
0
0
0
1
E
4
1
5
0
20.410362
21.908049
1.497687
E
4
4
0
0
0
...
0
0
0
1
0
0
1
E
4
1
6
0
21.908049
23.370907
1.462858
E:7/3
4
8
0
0
1
...
0
0
0
1
0
0
1
E
4
1
7
0
23.370907
24.856984
1.486077
A
9
9
0
1
0
...
0
0
0
0
1
0
0
E
4
1
8
0
24.856984
26.343061
1.486077
A:min/b3
9
0
1
0
0
...
0
0
0
0
1
0
0
E
4
1
9
0
26.343061
27.840748
1.497687
E
4
4
0
0
0
...
0
0
0
1
0
0
1
E
4
1
10
0
27.840748
29.350045
1.509297
B
11
11
0
0
0
...
0
1
0
0
0
0
1
E
4
1
11
0
29.350045
35.305963
5.955918
E
4
4
0
0
0
...
0
0
0
1
0
0
1
E
4
1
12
0
35.305963
36.803650
1.497687
A
9
9
0
1
0
...
0
0
0
0
1
0
0
E
4
1
13
0
36.803650
41.263102
4.459452
E
4
4
0
0
0
...
0
0
0
1
0
0
1
E
4
1
14
0
41.263102
44.245646
2.982544
B
11
11
0
0
0
...
0
1
0
0
0
0
1
E
4
1
15
0
44.245646
45.720113
1.474467
E
4
4
0
0
0
...
0
0
0
1
0
0
1
E
4
1
16
0
45.720113
47.206190
1.486077
E:7/3
4
8
0
0
1
...
0
0
0
1
0
0
1
E
4
1
17
0
47.206190
48.692267
1.486077
A
9
9
0
1
0
...
0
0
0
0
1
0
0
E
4
1
18
0
48.692267
50.155124
1.462857
A:min/b3
9
0
1
0
0
...
0
0
0
0
1
0
0
E
4
1
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0
50.155124
51.652811
1.497687
E
4
4
0
0
0
...
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0
0
1
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4
1
20
0
51.652811
53.138888
1.486077
B
11
11
0
0
0
...
0
1
0
0
0
0
1
E
4
1
21
0
53.138888
56.111043
2.972155
E
4
4
0
0
0
...
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0
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4
1
22
0
56.111043
65.131995
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9
9
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1
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0
0
0
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1
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4
1
23
0
65.131995
68.150589
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11
11
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0
1
0
0
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4
1
24
0
68.150589
71.192403
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A
9
9
0
1
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...
0
0
0
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1
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4
1
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0
71.192403
74.199387
3.006984
E
4
4
0
0
0
...
0
0
0
1
0
0
1
E
4
1
26
0
74.199387
75.697074
1.497687
A
9
9
0
1
0
...
0
0
0
0
1
0
0
E
4
1
27
0
75.697074
80.236575
4.539501
E
4
4
0
0
0
...
0
0
0
1
0
0
1
E
4
1
28
0
80.236575
83.208730
2.972155
B
11
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0
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...
0
1
0
0
0
0
1
E
4
1
29
0
83.208730
86.221693
3.012963
E
4
4
0
0
0
...
0
0
0
1
0
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1
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4
1
30
0
86.221693
87.736621
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A
9
9
0
1
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...
0
0
0
0
1
0
0
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4
1
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
14591
179
120.053537
122.004013
1.950476
D
2
2
0
0
1
...
0
1
0
0
1
0
0
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2
249
14592
179
122.004013
122.967641
0.963628
A
9
9
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1
0
...
0
0
0
0
1
0
0
D
2
249
14593
179
122.967641
123.942879
0.975238
G/2
7
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0
1
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0
0
1
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1
0
1
D
2
249
14594
179
123.942879
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A:7
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1
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1
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2
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14595
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127.843832
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2
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14596
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9
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2
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14597
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130.792766
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G/2
7
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0
1
D
2
249
14598
179
131.245555
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D/5
2
9
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0
1
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0
1
0
0
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D
2
249
14599
179
131.756394
135.622517
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A:7
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1
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0
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1
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2
249
14600
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135.622517
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D
2
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1
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2
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14601
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137.549773
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2
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14602
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14603
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2
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14605
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146.245646
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G/2
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1
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D
2
249
14606
179
146.733265
147.244104
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D/5
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D
2
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14607
179
147.244104
151.110226
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A
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14608
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2
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14610
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154.024331
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G/2
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2
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14611
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A:7
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2
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14616
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162.174535
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D:7
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2
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14620
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2
249
14441 rows × 22 columns
In [96]:
# track_chords.groupby(['track_id']).last()['key_index']
(chords_all.groupby(['track_id', 'key_index']).count()['start'] > 16).mean()
Out[96]:
0.76305220883534142
In [222]:
c = chords_all[:200]
c
Out[222]:
track_id
start
end
duration
label
root
bass
C
Db
D
...
F
Gb
G
Ab
A
Bb
B
key_diatonic_root_label
key_diatonic_root
key_index
1
0
2.612267
11.459070
8.846803
E
4
4
0
0
0
...
0
0
0
1
0
0
1
E
4
1
2
0
11.459070
12.921927
1.462857
A
9
9
0
1
0
...
0
0
0
0
1
0
0
E
4
1
3
0
12.921927
17.443474
4.521547
E
4
4
0
0
0
...
0
0
0
1
0
0
1
E
4
1
4
0
17.443474
20.410362
2.966888
B
11
11
0
0
0
...
0
1
0
0
0
0
1
E
4
1
5
0
20.410362
21.908049
1.497687
E
4
4
0
0
0
...
0
0
0
1
0
0
1
E
4
1
6
0
21.908049
23.370907
1.462858
E:7/3
4
8
0
0
1
...
0
0
0
1
0
0
1
E
4
1
7
0
23.370907
24.856984
1.486077
A
9
9
0
1
0
...
0
0
0
0
1
0
0
E
4
1
8
0
24.856984
26.343061
1.486077
A:min/b3
9
0
1
0
0
...
0
0
0
0
1
0
0
E
4
1
9
0
26.343061
27.840748
1.497687
E
4
4
0
0
0
...
0
0
0
1
0
0
1
E
4
1
10
0
27.840748
29.350045
1.509297
B
11
11
0
0
0
...
0
1
0
0
0
0
1
E
4
1
11
0
29.350045
35.305963
5.955918
E
4
4
0
0
0
...
0
0
0
1
0
0
1
E
4
1
12
0
35.305963
36.803650
1.497687
A
9
9
0
1
0
...
0
0
0
0
1
0
0
E
4
1
13
0
36.803650
41.263102
4.459452
E
4
4
0
0
0
...
0
0
0
1
0
0
1
E
4
1
14
0
41.263102
44.245646
2.982544
B
11
11
0
0
0
...
0
1
0
0
0
0
1
E
4
1
15
0
44.245646
45.720113
1.474467
E
4
4
0
0
0
...
0
0
0
1
0
0
1
E
4
1
16
0
45.720113
47.206190
1.486077
E:7/3
4
8
0
0
1
...
0
0
0
1
0
0
1
E
4
1
17
0
47.206190
48.692267
1.486077
A
9
9
0
1
0
...
0
0
0
0
1
0
0
E
4
1
18
0
48.692267
50.155124
1.462857
A:min/b3
9
0
1
0
0
...
0
0
0
0
1
0
0
E
4
1
19
0
50.155124
51.652811
1.497687
E
4
4
0
0
0
...
0
0
0
1
0
0
1
E
4
1
20
0
51.652811
53.138888
1.486077
B
11
11
0
0
0
...
0
1
0
0
0
0
1
E
4
1
21
0
53.138888
56.111043
2.972155
E
4
4
0
0
0
...
0
0
0
1
0
0
1
E
4
1
22
0
56.111043
65.131995
9.020952
A
9
9
0
1
0
...
0
0
0
0
1
0
0
E
4
1
23
0
65.131995
68.150589
3.018594
B
11
11
0
0
0
...
0
1
0
0
0
0
1
E
4
1
24
0
68.150589
71.192403
3.041814
A
9
9
0
1
0
...
0
0
0
0
1
0
0
E
4
1
25
0
71.192403
74.199387
3.006984
E
4
4
0
0
0
...
0
0
0
1
0
0
1
E
4
1
26
0
74.199387
75.697074
1.497687
A
9
9
0
1
0
...
0
0
0
0
1
0
0
E
4
1
27
0
75.697074
80.236575
4.539501
E
4
4
0
0
0
...
0
0
0
1
0
0
1
E
4
1
28
0
80.236575
83.208730
2.972155
B
11
11
0
0
0
...
0
1
0
0
0
0
1
E
4
1
29
0
83.208730
86.221693
3.012963
E
4
4
0
0
0
...
0
0
0
1
0
0
1
E
4
1
30
0
86.221693
87.736621
1.514928
A
9
9
0
1
0
...
0
0
0
0
1
0
0
E
4
1
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
174
2
157.240183
159.480454
2.240271
D
2
2
0
0
1
...
0
1
0
0
1
0
0
D
2
3
175
2
159.480454
161.624986
2.144532
B:min
11
11
0
0
1
...
0
1
0
0
0
0
1
D
2
3
176
2
161.624986
163.941847
2.316861
D
2
2
0
0
1
...
0
1
0
0
1
0
0
D
2
3
177
2
163.941847
165.990642
2.048795
B:min
11
11
0
0
1
...
0
1
0
0
0
0
1
D
2
3
178
2
165.990642
168.173469
2.182827
D
2
2
0
0
1
...
0
1
0
0
1
0
0
D
2
3
179
2
168.173469
170.528625
2.355156
B:min
11
11
0
0
1
...
0
1
0
0
0
0
1
D
2
3
180
2
170.528625
174.530476
4.001851
D
2
2
0
0
1
...
0
1
0
0
1
0
0
D
2
3
182
3
0.000000
0.459543
0.459543
N
0
0
0
0
0
...
0
0
0
0
0
0
0
Bb
10
4
183
3
0.459543
7.639897
7.180354
Bb
10
10
0
0
1
...
1
0
0
0
0
1
0
Bb
10
4
184
3
7.639897
14.977403
7.337506
Bb
10
10
0
0
1
...
1
0
0
0
0
1
0
Bb
10
4
185
3
14.977403
18.646156
3.668753
Eb:7
3
3
0
1
0
...
0
0
1
0
0
1
0
Bb
10
4
186
3
18.646156
22.338129
3.691973
Bb
10
10
0
0
1
...
1
0
0
0
0
1
0
Bb
10
4
187
3
22.338129
24.184115
1.845986
F:9
5
5
1
0
0
...
1
0
1
0
1
0
0
Bb
10
4
188
3
24.184115
26.041712
1.857597
Eb:9
3
3
0
1
0
...
1
0
1
0
0
1
0
Bb
10
4
189
3
26.041712
27.910918
1.869206
Bb
10
10
0
0
1
...
1
0
0
0
0
1
0
Bb
10
4
190
3
27.910918
29.780124
1.869206
F
5
5
1
0
0
...
1
0
0
0
1
0
0
Bb
10
4
191
3
29.780124
37.106020
7.325896
Bb
10
10
0
0
1
...
1
0
0
0
0
1
0
Bb
10
4
192
3
37.106020
40.797993
3.691973
Eb:7
3
3
0
1
0
...
0
0
1
0
0
1
0
Bb
10
4
193
3
40.797993
44.466746
3.668753
Bb
10
10
0
0
1
...
1
0
0
0
0
1
0
Bb
10
4
194
3
44.466746
46.324342
1.857596
F:9
5
5
1
0
0
...
1
0
1
0
1
0
0
Bb
10
4
195
3
46.324342
48.170328
1.845986
Eb:9
3
3
0
1
0
...
1
0
1
0
0
1
0
Bb
10
4
196
3
48.170328
49.981485
1.811157
Bb
10
10
0
0
1
...
1
0
0
0
0
1
0
Bb
10
4
197
3
49.981485
51.839081
1.857596
Bb:7
10
10
0
0
1
...
1
0
0
1
0
1
0
Bb
10
4
198
3
51.839081
55.589104
3.750023
Eb
3
3
0
0
0
...
0
0
1
0
0
1
0
Bb
10
4
199
3
55.589104
59.292687
3.703583
Bb
10
10
0
0
1
...
1
0
0
0
0
1
0
Bb
10
4
200
3
59.292687
63.042709
3.750022
Eb
3
3
0
0
0
...
0
0
1
0
0
1
0
Bb
10
4
201
3
63.042709
66.734682
3.691973
F
5
5
1
0
0
...
1
0
0
0
1
0
0
Bb
10
4
202
3
66.734682
74.118628
7.383946
Bb
10
10
0
0
1
...
1
0
0
0
0
1
0
Bb
10
4
203
3
74.118628
77.857040
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77.857040
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200 rows × 22 columns
In [217]:
def pcs_block_cols(block_size):
return ['%s_%02d'%(pc, i) for i in range(block_size) for pc in pcs_columns]
def block_columns(block_size):
return pcs_block_cols(block_size) + ['key_diatonic_root']
def merge_chord_block(block_df):
all_pcs = block_df.as_matrix(columns=pcs_columns).ravel()
most_frequent_key = block_df['key_diatonic_root'].value_counts().index[0]
return np.hstack([all_pcs, most_frequent_key])
In [252]:
def roll_chords(chords_df, window_size=4):
blocks = (chords_df.iloc[start:start+window_size] for start in range(len(chords_df) - window_size + 1))
c_rolling = (merge_chord_block(block) for block in blocks)
df_rolling = pd.DataFrame(c_rolling, columns=block_columns(window_size)).astype(np.int16)
return df_rolling
Generate data points by reshaping input rows in the rolling window and selecting the most frequent output label. Do this for both original and synthetic data and for a different window sizes.
In [256]:
# TODO: optimize this, since it is not really efficient (~20 minutes for all the files...)
In [254]:
for postfix, chords in [('', chords_all), ('_synth', chords_all_synth)]:
for window in (1,2,4,8,16):
print('window:', window)
chords_rolling = roll_chords(chords, window_size=window)
print('shape:', chords_rolling.shape)
chords_rolling.to_csv('data/beatles/derived/all_chords_with_keys'+postfix+'_rolling_'+str(window)+'.tsv', sep='\t', index=False)
window: 1
shape: (14441, 13)
window: 2
shape: (14440, 25)
window: 4
shape: (14438, 49)
window: 8
shape: (14434, 97)
window: 16
shape: (14426, 193)
window: 1
shape: (173292, 13)
window: 2
shape: (173291, 25)
window: 4
shape: (173289, 49)
window: 8
shape: (173285, 97)
window: 16
shape: (173277, 193)
In [ ]:
Content source: bzamecnik/ml-playground
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