-
Notifications
You must be signed in to change notification settings - Fork 1
/
templates.py
396 lines (303 loc) · 17.8 KB
/
templates.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
# coding=utf-8
import numpy as np
from scipy.stats import pearsonr
# Essentia's algorithm had a function to resize pcp's to fit the key profiles
# consider implementing this in the future
# podríamos generar perfiles que, una vez extraídas sus características modales,
# maximicen la diferencia entre ellos
# it is going to be sensible as to whether we start counting on A or on C... I would suggest C, thoguh
key_names = ["C", "C#", "D", "Eb", "E", "F", "F#", "G", "Ab", "A", "Bb", "B"] # ELSE
# key_names = ["A", "Bb", "B", "C", "C#", "D", "Eb", "E", "F", "F#", "G", "Ab"] # ESSENTIA
def template_matching_2(pcp, profile_type='bgate'):
key_templates = {
'bgate': np.array([[1., 0.00, 0.42, 0.00, 0.53, 0.37, 0.00, 0.77, 0.00, 0.38, 0.21, 0.30],
[1., 0.00, 0.36, 0.39, 0.00, 0.38, 0.00, 0.74, 0.27, 0.00, 0.42, 0.23]]),
# almost identical to bgate. kept for backwards compatibility
'bmtg3': np.array([[1.00, 0.00, 0.42, 0.00, 0.53, 0.37, 0.00, 0.76, 0.00, 0.38, 0.21, 0.30],
[1.00, 0.00, 0.36, 0.39, 0.10, 0.37, 0.00, 0.76, 0.27, 0.00, 0.42, 0.23]]),
'bmtg2': np.array([[1.00, 0.10, 0.42, 0.10, 0.53, 0.37, 0.10, 0.77, 0.10, 0.38, 0.21, 0.30],
[1.00, 0.10, 0.36, 0.39, 0.29, 0.38, 0.10, 0.74, 0.27, 0.10, 0.42, 0.23]]),
# was originally bmtg1
'braw': np.array([[1., 0.1573, 0.4200, 0.1570, 0.5296, 0.3669, 0.1632, 0.7711, 0.1676, 0.3827, 0.2113, 0.2965],
[1., 0.2330, 0.3615, 0.3905, 0.2925, 0.3777, 0.1961, 0.7425, 0.2701, 0.2161, 0.4228, 0.2272]]),
'diatonic': np.array([[1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1],
[1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1]]),
'monotonic': np.array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]]),
'triads': np.array([[1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0],
[1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0]]),
'edma_ecir': np.array([[0.16519551, 0.04749026, 0.08293076, 0.06687112, 0.09994645, 0.09274123, 0.05294487, 0.13159476, 0.05218986, 0.07443653, 0.06940723, 0.0642515],
[0.17235348, 0.05336489, 0.0761009, 0.10043649, 0.05621498, 0.08527853, 0.0497915, 0.13451001, 0.07458916, 0.05003023, 0.09187879, 0.05545106]]),
'edmm_ecir': np.array([[0.083, 0.083, 0.083, 0.083, 0.083, 0.083, 0.083, 0.083, 0.083, 0.083, 0.083, 0.083],
[0.17235348, 0.04, 0.0761009, 0.12, 0.05621498, 0.08527853, 0.0497915, 0.13451001, 0.07458916, 0.05003023, 0.09187879, 0.05545106]]),
'edma': np.array([[1., 0.2875, 0.5020, 0.4048, 0.6050, 0.5614, 0.3205, 0.7966, 0.3159, 0.4506, 0.4202, 0.3889],
[1., 0.3096, 0.4415, 0.5827, 0.3262, 0.4948, 0.2889, 0.7804, 0.4328, 0.2903, 0.5331, 0.3217]]),
'edmm': np.array([[1., 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],
[1., 0.2321, 0.4415, 0.6962, 0.3262, 0.4948, 0.2889, 0.7804, 0.4328, 0.2903, 0.5331, 0.3217]]),
'krumhansl': np.array([[6.35, 2.23, 3.48, 2.33, 4.38, 4.09, 2.52, 5.19, 2.39, 3.66, 2.29, 2.88],
[6.33, 2.68, 3.52, 5.38, 2.60, 3.53, 2.54, 4.75, 3.98, 2.69, 3.34, 3.17]]),
'temperley99': np.array([[5.0, 2.0, 3.5, 2.0, 4.5, 4.0, 2.0, 4.5, 2.0, 3.5, 1.5, 4.0],
[5.0, 2.0, 3.5, 4.5, 2.0, 4.0, 2.0, 4.5, 3.5, 2.0, 1.5, 4.0]]),
'temperley05': np.array([[0.748, 0.060, 0.488, 0.082, 0.67, 0.46, 0.096, 0.715, 0.104, 0.366, 0.057, 0.4],
[0.712, 0.084, 0.474, 0.618, 0.049, 0.46, 0.105, 0.747, 0.404, 0.067, 0.133, 0.33]]),
'temperley-essen': np.array([[0.184, 0.001, 0.155, 0.003, 0.191, 0.109, 0.005, 0.214, 0.001, 0.078, 0.004, 0.055],
[0.192, 0.005, 0.149, 0.179, 0.002, 0.144, 0.002, 0.201, 0.038, 0.012, 0.053, 0.022]]),
'thpcp': np.array([[0.95162, 0.20742, 0.71758, 0.22007, 0.71341, 0.48841, 0.31431, 1.00000, 0.20957, 0.53657, 0.22585, 0.55363],
[0.94409, 0.21742, 0.64525, 0.63229, 0.27897, 0.57709, 0.26428, 1.0000, 0.26428, 0.30633, 0.45924, 0.35929]]),
'shaath': np.array([[6.6, 2.0, 3.5, 2.3, 4.6, 4.0, 2.5, 5.2, 2.4, 3.7, 2.3, 3.4],
[6.5, 2.7, 3.5, 5.4, 2.6, 3.5, 2.5, 5.2, 4.0, 2.7, 4.3, 3.2]]),
'gomez': np.array([[0.82, 0.00, 0.55, 0.00, 0.53, 0.30, 0.08, 1.00, 0.00, 0.38, 0.00, 0.47],
[0.81, 0.00, 0.53, 0.54, 0.00, 0.27, 0.07, 1.00, 0.27, 0.07, 0.10, 0.36]]),
'faraldo': np.array([[7.0, 2.0, 3.8, 2.3, 4.7, 4.1, 2.5, 5.2, 2.0, 3.7, 3.0, 3.4],
[7.0, 3.0, 3.8, 4.5, 2.6, 3.5, 2.5, 5.2, 4.0, 2.5, 4.5, 3.0]]),
'pentatonic': np.array([[1.0, 0.1, 0.25, 0.1, 0.5, 0.7, 0.1, 0.8, 0.1, 0.25, 0.1, 0.5],
[1.0, 0.2, 0.25, 0.5, 0.1, 0.7, 0.1, 0.8, 0.3, 0.2, 0.6, 0.2]]),
'noland': np.array([[0.0629, 0.0146, 0.061, 0.0121, 0.0623, 0.0414, 0.0248, 0.0631, 0.015, 0.0521, 0.0142, 0.0478],
[0.0682, 0.0138, 0.0543, 0.0519, 0.0234, 0.0544, 0.0176, 0.067, 0.0349, 0.0297, 0.0401, 0.027]])
}
if (pcp.size < 12) or (pcp.size % 12 != 0):
raise IndexError("Input PCP size is not a positive multiple of 12")
_major, _minor = _select_profile_type(profile_type, key_templates)
first_max_major = -1
second_max_major = -1
key_index_major = -1
first_max_minor = -1
second_max_minor = -1
key_index_minor = -1
for shift in np.arange(pcp.size):
correlation_major = (pearsonr(pcp, np.roll(_major, shift)))[0]
if correlation_major > first_max_major:
second_max_major = first_max_major
first_max_major = correlation_major
key_index_major = shift
correlation_minor = (pearsonr(pcp, np.roll(_minor, shift)))[0]
if correlation_minor > first_max_minor:
second_max_minor = first_max_minor
first_max_minor = correlation_minor
key_index_minor = shift
if first_max_major > first_max_minor:
key_index = key_index_major
scale = 'major'
first_max = first_max_major
second_max = second_max_major
elif first_max_minor > first_max_major:
key_index = key_index_minor
scale = 'minor'
first_max = first_max_minor
second_max = second_max_minor
else:
key_index = -1
first_max = -1
second_max = -1
scale = 'unknown'
if key_index < 0:
raise IndexError("key_index smaller than zero. Could not find key.")
else:
first_to_second_ratio = (first_max - second_max) / first_max
return key_names[int(key_index)], scale, first_max, first_to_second_ratio
def template_matching_3(pcp, profile_type='bgate'):
if (pcp.size < 12) or (pcp.size % 12 != 0):
raise IndexError("Input PCP size is not a positive multiple of 12")
key_templates = {
'bgate': np.array([[1., 0.00, 0.42, 0.00, 0.53, 0.37, 0.00, 0.77, 0.00, 0.38, 0.21, 0.30],
[1., 0.00, 0.36, 0.39, 0.00, 0.38, 0.00, 0.74, 0.27, 0.00, 0.42, 0.23],
[1., 0.26, 0.35, 0.29, 0.44, 0.36, 0.21, 0.78, 0.26, 0.25, 0.32, 0.26]]),
# almost identical to bgate, predecessor.
'bmtg3': np.array([[1., 0.00, 0.42, 0.00, 0.53, 0.37, 0.00, 0.76, 0.00, 0.38, 0.21, 0.30],
[1., 0.00, 0.36, 0.39, 0.10, 0.37, 0.00, 0.76, 0.27, 0.00, 0.42, 0.23],
[1., 0.26, 0.35, 0.29, 0.44, 0.37, 0.21, 0.76, 0.26, 0.25, 0.32, 0.26]]),
'bmtg2': np.array([[1., 0.10, 0.42, 0.10, 0.53, 0.37, 0.10, 0.77, 0.10, 0.38, 0.21, 0.30],
[1., 0.10, 0.36, 0.39, 0.29, 0.38, 0.10, 0.74, 0.27, 0.10, 0.42, 0.23],
[1., 0.26, 0.35, 0.29, 0.44, 0.36, 0.21, 0.78, 0.26, 0.25, 0.32, 0.26]]),
# was bmtg1
'braw': np.array([[1., 0.1573, 0.4200, 0.1570, 0.5296, 0.3669, 0.1632, 0.7711, 0.1676, 0.3827, 0.2113, 0.2965],
[1., 0.2330, 0.3615, 0.3905, 0.2925, 0.3777, 0.1961, 0.7425, 0.2701, 0.2161, 0.4228, 0.2272],
[1., 0.2608, 0.3528, 0.2935, 0.4393, 0.3580, 0.2137, 0.7809, 0.2578, 0.2539, 0.3233, 0.2615]]),
'edma': np.array([[1.00, 0.29, 0.50, 0.40, 0.60, 0.56, 0.32, 0.80, 0.31, 0.45, 0.42, 0.39],
[1.00, 0.31, 0.44, 0.58, 0.33, 0.49, 0.29, 0.78, 0.43, 0.29, 0.53, 0.32],
[1.00, 0.26, 0.35, 0.29, 0.44, 0.36, 0.21, 0.78, 0.26, 0.25, 0.32, 0.26]])
}
_major, _minor, _minor2 = _select_profile_type(profile_type, key_templates)
first_max_major = -1
second_max_major = -1
key_index_major = -1
first_max_minor = -1
second_max_minor = -1
key_index_minor = -1
first_max_minor2 = -1
second_max_minor2 = -1
key_index_minor2 = -1
for shift in np.arange(pcp.size):
correlation_major = (pearsonr(pcp, np.roll(_major, shift)))[0]
if correlation_major > first_max_major:
second_max_major = first_max_major
first_max_major = correlation_major
key_index_major = shift
correlation_minor = (pearsonr(pcp, np.roll(_minor, shift)))[0]
if correlation_minor > first_max_minor:
second_max_minor = first_max_minor
first_max_minor = correlation_minor
key_index_minor = shift
correlation_minor2 = (pearsonr(pcp, np.roll(_minor2, shift)))[0]
if correlation_minor2 > first_max_minor2:
second_max_minor2 = first_max_minor2
first_max_minor2 = correlation_minor2
key_index_minor2 = shift
if (first_max_major > first_max_minor) and (first_max_major > first_max_minor2):
key_index = key_index_major
scale = 'major'
first_max = first_max_major
second_max = second_max_major
elif (first_max_minor >= first_max_major) and (first_max_minor >= first_max_minor2):
key_index = key_index_minor
scale = 'minor'
first_max = first_max_minor
second_max = second_max_minor
elif (first_max_minor2 > first_max_major) and (first_max_minor2 > first_max_minor):
key_index = key_index_minor2
scale = 'minor'
first_max = first_max_minor2
second_max = second_max_minor2
else:
key_index = -1
first_max = -1
second_max = -1
scale = 'unknown'
if key_index < 0:
raise IndexError("key_index smaller than zero. Could not find key.")
else:
first_to_second_ratio = (first_max - second_max) / first_max
return key_names[int(key_index)], scale, first_max, first_to_second_ratio
def template_matching_modal(pcp):
if (pcp.size < 12) or (pcp.size % 12 != 0):
raise IndexError("Input PCP size is not a positive multiple of 12")
key_templates = {
'ionian': np.array([1.00, 0.10, 0.43, 0.14, 0.61, 0.38, 0.12, 0.78, 0.13, 0.46, 0.15, 0.60]),
'harmonic': np.array([1.00, 0.10, 0.36, 0.37, 0.22, 0.33, 0.18, 0.75, 0.25, 0.18, 0.37, 0.37]),
'mixolydian': np.array([1.00, 0.10, 0.42, 0.10, 0.55, 0.40, 0.10, 0.77, 0.10, 0.42, 0.66, 0.15]),
'phrygian': np.array([1.00, 0.47, 0.10, 0.36, 0.24, 0.37, 0.16, 0.76, 0.30, 0.20, 0.45, 0.23]),
'fifth': np.array([1.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.65, 0.00, 0.00, 0.00, 0.00]),
'monotonic': np.array([1.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00]),
'difficult': np.array([0.80, 0.60, 0.80, 0.60, 0.80, 0.60, 0.80, 0.60, 0.80, 0.60, 0.80, 0.60]),
}
first_max_ionian = -1
second_max_ionian = -1
key_index_ionian = -1
first_max_harmonic = -1
second_max_harmonic = -1
key_index_harmonic = -1
first_max_mixolydian = -1
second_max_mixolydian = -1
key_index_mixolydian = -1
first_max_phrygian = -1
second_max_phrygian = -1
key_index_phrygian = -1
first_max_fifth = -1
second_max_fifth = -1
key_index_fifth = -1
first_max_monotonic = -1
second_max_monotonic = -1
key_index_monotonic = -1
first_max_difficult = -1
second_max_difficult = -1
key_index_difficult = -1
for shift in np.arange(pcp.size):
correlation_ionian = (pearsonr(pcp, np.roll(key_templates['ionian'], shift)))[0]
if correlation_ionian > first_max_ionian:
second_max_ionian = first_max_ionian
first_max_ionian = correlation_ionian
key_index_ionian = shift
correlation_harmonic = (pearsonr(pcp, np.roll(key_templates['harmonic'], shift)))[0]
if correlation_harmonic > first_max_harmonic:
second_max_harmonic = first_max_harmonic
first_max_harmonic = correlation_harmonic
key_index_harmonic = shift
correlation_mixolydian = (pearsonr(pcp, np.roll(key_templates['mixolydian'], shift)))[0]
if correlation_mixolydian > first_max_mixolydian:
second_max_mixolydian = first_max_mixolydian
first_max_mixolydian = correlation_mixolydian
key_index_mixolydian = shift
correlation_phrygian = (pearsonr(pcp, np.roll(key_templates['phrygian'], shift)))[0]
if correlation_phrygian > first_max_phrygian:
second_max_phrygian = first_max_phrygian
first_max_phrygian = correlation_phrygian
key_index_phrygian = shift
correlation_fifth = (pearsonr(pcp, np.roll(key_templates['fifth'], shift)))[0]
if correlation_fifth > first_max_fifth:
second_max_fifth = first_max_fifth
first_max_fifth = correlation_fifth
key_index_fifth = shift
correlation_monotonic = (pearsonr(pcp, np.roll(key_templates['monotonic'], shift)))[0]
if correlation_monotonic > first_max_monotonic:
second_max_monotonic = first_max_monotonic
first_max_monotonic = correlation_monotonic
key_index_monotonic = shift
correlation_difficult = (pearsonr(pcp, np.roll(key_templates['difficult'], shift)))[0]
if correlation_difficult > first_max_difficult:
second_max_difficult = first_max_difficult
first_max_difficult = correlation_difficult
key_index_difficult = shift
if (first_max_ionian > first_max_harmonic) and (first_max_ionian > first_max_mixolydian) \
and (first_max_ionian > first_max_phrygian) and (first_max_ionian > first_max_fifth) \
and (first_max_ionian > first_max_monotonic) and (first_max_ionian > first_max_difficult):
key_index = key_index_ionian
scale = 'ionian'
first_max = first_max_ionian
second_max = second_max_ionian
elif (first_max_harmonic > first_max_ionian) and (first_max_harmonic > first_max_mixolydian) \
and (first_max_harmonic > first_max_phrygian) and (first_max_harmonic > first_max_fifth) \
and (first_max_harmonic > first_max_monotonic) and (first_max_harmonic > first_max_difficult):
key_index = key_index_harmonic
scale = 'harmonic'
first_max = first_max_harmonic
second_max = second_max_harmonic
elif (first_max_mixolydian > first_max_harmonic) and (first_max_mixolydian > first_max_ionian) \
and (first_max_mixolydian > first_max_phrygian) and (first_max_mixolydian > first_max_fifth) \
and (first_max_mixolydian > first_max_monotonic) and (first_max_mixolydian > first_max_difficult):
key_index = key_index_mixolydian
scale = 'mixolydian'
first_max = first_max_mixolydian
second_max = second_max_mixolydian
elif (first_max_phrygian > first_max_harmonic) and (first_max_phrygian > first_max_mixolydian) \
and (first_max_phrygian > first_max_ionian) and (first_max_phrygian > first_max_fifth) \
and (first_max_phrygian > first_max_monotonic) and (first_max_phrygian > first_max_difficult):
key_index = key_index_phrygian
scale = 'phrygian'
first_max = first_max_phrygian
second_max = second_max_phrygian
elif (first_max_fifth > first_max_harmonic) and (first_max_fifth > first_max_mixolydian) \
and (first_max_fifth > first_max_phrygian) and (first_max_fifth > first_max_ionian) \
and (first_max_fifth > first_max_monotonic) and (first_max_fifth > first_max_difficult):
key_index = key_index_fifth
scale = 'fifth'
first_max = first_max_fifth
second_max = second_max_fifth
elif (first_max_monotonic > first_max_harmonic) and (first_max_monotonic > first_max_mixolydian) \
and (first_max_monotonic > first_max_phrygian) and (first_max_monotonic > first_max_fifth) \
and (first_max_monotonic > first_max_ionian) and (first_max_monotonic > first_max_difficult):
key_index = key_index_monotonic
scale = 'monotonic'
first_max = first_max_monotonic
second_max = second_max_monotonic
elif (first_max_difficult > first_max_harmonic) and (first_max_difficult > first_max_mixolydian) \
and (first_max_difficult > first_max_phrygian) and (first_max_difficult > first_max_fifth) \
and (first_max_difficult > first_max_monotonic) and (first_max_difficult > first_max_ionian):
key_index = key_index_difficult
scale = 'difficult'
first_max = first_max_difficult
second_max = second_max_difficult
else:
key_index = -1
first_max = -1
second_max = -1
scale = 'unknown'
if key_index < 0:
raise IndexError("key_index smaller than zero. Could not find key.")
else:
first_to_second_ratio = (first_max - second_max) / first_max
return key_names[int(key_index)], scale, first_max, first_to_second_ratio
def _select_profile_type(profile, templates_dict):
try:
return templates_dict[profile]
except:
raise KeyError("Unsupported profile: {0}\nvalid profiles are:\n{1}".format(profile, templates_dict.keys()))