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Notes

  • d2['file_scores'][k]['paths'] is a matrix of all-pairs segment-to-segment alignments
    • indices are relative to the segment in question
    • same..['scores'] is the alignment scores:
    • use < thresholds['none'] to find the good segment pairs
  • generate data from
    • isophonics functions
    • salami functions
    • l2 norm
  • samples:
    • features = patches of cqt +- 5 frames on either side
    • positives = drawn from pairs of segments below threshold
    • negatives = "" above the threshold
  • model evaluation
    • serra method or laplacian




In [1]:



    


import numpy as np
import librosa
import cPickle as pickle
import numba
import os
from joblib import Parallel, delayed



    











In [2]:



    


%matplotlib inline
import matplotlib.pyplot as plt



    











In [3]:



    


def symstack(X, n_steps=3, delay=1, **kwargs):
    '''Symmetric history stacking.
    
    like librosa.feature.stack_memory, but IN THE FUTURE!!!
    '''
    rpad = n_steps * delay
    Xpad = np.pad(X,
                  [(0, 0), (0, rpad)],
                  **kwargs)
    
    Xstack = librosa.feature.stack_memory(Xpad,
                                          n_steps=2 * n_steps + 1,
                                          delay=delay,
                                          **kwargs)
    
    return Xstack[:, rpad:]



    











In [4]:



    


def load_feature_data(file_name, n_steps=5):
    
    fname = '../data/features/{}.pk'.format(file_name)
    
    data = pickle.load(open(fname, 'r'))
    
    return symstack(data['cqgram'].astype(np.float32),
                    n_steps=n_steps,
                    mode='edge'), data['intframes']



    











In [5]:



    


@numba.jit
def similarity_paths(X_raw, t_i, t_j, n_samples, max_samples, only_one=True):
    
    X = np.empty((len(t_i), X_raw.shape[0]), dtype=X_raw.dtype)
    
    S = np.zeros((X_raw.shape[1], X_raw.shape[1]))
    
    k = 0
    used_once = np.zeros(X_raw.shape[1], dtype=bool)
    
    for i, j in zip(t_i, t_j):
        if n_samples[i] >= max_samples:
            continue
        
        if only_one and (used_once[i] or used_once[j]):
            continue
            
        S[i, j] = 1
        n_samples[i] += 1
        n_samples[j] += 1
        used_once[i] = True
        used_once[j] = True
        
        #X[k] = np.abs(X_raw[:, i] - X_raw[:, j])
        #k = k + 1
    
    return S



    











In [6]:



    


import scipy.spatial



    











In [294]:



    


def plot_selfsim(max_samples=4,
                       paths=None,
                       file_name=None,
                       thresholds=None,
                       labels=None,
                       intervals=None,
                       fmeasures=None,
                       scores=None):
    
    # intframes gives the section->cqt frame alignments
    X_raw, intframes = load_feature_data(file_name)
    
    thresh = thresholds['none']
    
    n_segments = len(paths)
    
    n_samples = np.zeros((2, X_raw.shape[1]))
    
    ex_x, ex_y = [], []
    
    S = np.zeros((X_raw.shape[1], X_raw.shape[1]))
    
    for seg_i in range(n_segments):
        off_i = intframes[seg_i, 0]
        
        for seg_j in range(seg_i + 1, len(paths[seg_i])):
            off_j = intframes[seg_j, 0]
            
            
            if not isinstance(paths[seg_i][seg_j], list):
                continue
            
            if not np.isfinite(scores[seg_i, seg_j]):
                continue
            
            if scores[seg_i, seg_j] <= thresh:
                y = 1
            else:
                y = -1
            
            
            # Pull out features from the path
            t_i, t_j = paths[seg_i][seg_j]
            t_i = t_i + off_i
            t_j = t_j + off_j
            S += y * similarity_paths(X_raw, t_i, t_j, n_samples[y], max_samples, only_one=(y>0))
    
    return S + S.T, scipy.spatial.distance.cdist(X_raw.T, X_raw.T, metric='seuclidean'), intframes



    











In [8]:



    


@numba.jit
def get_feature_paths(X_raw, t_i, t_j, n_samples, max_samples, only_one=True):
    
    X = np.empty((len(t_i), X_raw.shape[0]), dtype=X_raw.dtype)
    
    k = 0
    used_once = np.zeros(X_raw.shape[1], dtype=bool)
    
    for i, j in zip(t_i, t_j):
        if n_samples[i] >= max_samples:
            continue
            
        if only_one and (used_once[i] or used_once[j]):
            continue
            
        n_samples[i] += 1
        n_samples[j] += 1
        
        used_once[i] = True
        used_once[j] = True
        
        X[k] = np.abs(X_raw[:, i] - X_raw[:, j])
        k = k + 1
    
    return X[:k]



    











In [306]:



    


def make_training_data(max_samples=4,
                       paths=None,
                       file_name=None,
                       thresholds=None,
                       labels=None,
                       intervals=None,
                       fmeasures=None,
                       scores=None):
    
    # intframes gives the section->cqt frame alignments
    X_raw, intframes = load_feature_data(file_name)
    
    thresh = thresholds['none']
    
    n_segments = len(paths)
    
    n_samples = np.zeros((2, X_raw.shape[1]))
    
    ex_x, ex_y = [], []
    
    for seg_i in range(n_segments):
        off_i = intframes[seg_i, 0]
        
        for seg_j in range(seg_i + 1, len(paths[seg_i])):
            off_j = intframes[seg_j, 0]
            
            if not isinstance(paths[seg_i][seg_j], list):
                continue
            if not np.isfinite(scores[seg_i, seg_j]):
                continue
                
            if scores[seg_i, seg_j] <= thresh:
                y = 1
            else:
                y = 0
                
            # Pull out features from the path
            t_i, t_j = paths[seg_i][seg_j]
            t_i = t_i + off_i
            t_j = t_j + off_j
            
            X = get_feature_paths(X_raw, t_i, t_j, n_samples[y], max_samples, only_one=y)
            ex_x.append(X)
            ex_y.append(y * np.ones(len(X), dtype=np.int32))
    
    if len(ex_x):
        ex_x = np.concatenate(ex_x)
        ex_y = np.concatenate(ex_y)

    else:
        ex_x = np.empty( (0, X_raw.shape[0]), dtype=X_raw.dtype)
        ex_y = np.empty( (0, 1), dtype=np.int32)
        
    return ex_x, ex_y



    











In [307]:



    


import os
def process_file(file_scores, max_samples=8):
    
    if file_scores['paths']:
        X, Y = make_training_data(max_samples=max_samples,
                                  **file_scores)
        outfile = '../data/labeled_features/{}_beats.npz'.format(os.path.basename(file_scores['file_name']))
        np.savez(outfile, X=X, Y=Y)



    











In [327]:



    


all_data = pickle.load(open('../data/scores_datasetESALAMI_levelEfunction_distEL2.pk'))
#all_data = pickle.load(open('../data/scores_datasetEIsophonics_levelEfunction_distEL2.pk'))



    











In [328]:



    


[(x, y['file_name']) for (x,y) in enumerate(all_data['file_scores'])]



    


















    
Out[328]:








[(0, 'SALAMI_396.mp3'),
 (1, 'SALAMI_822.mp3'),
 (2, 'SALAMI_748.mp3'),
 (3, 'SALAMI_564.mp3'),
 (4, 'SALAMI_584.mp3'),
 (5, 'SALAMI_550.mp3'),
 (6, 'SALAMI_1028.mp3'),
 (7, 'SALAMI_1300.mp3'),
 (8, 'SALAMI_248.mp3'),
 (9, 'SALAMI_960.mp3'),
 (10, 'SALAMI_1562.mp3'),
 (11, 'SALAMI_486.mp3'),
 (12, 'SALAMI_1180.mp3'),
 (13, 'SALAMI_990.mp3'),
 (14, 'SALAMI_1462.mp3'),
 (15, 'SALAMI_304.mp3'),
 (16, 'SALAMI_40.mp3'),
 (17, 'SALAMI_356.mp3'),
 (18, 'SALAMI_540.mp3'),
 (19, 'SALAMI_634.mp3'),
 (20, 'SALAMI_970.mp3'),
 (21, 'SALAMI_362.mp3'),
 (22, 'SALAMI_1394.mp3'),
 (23, 'SALAMI_772.mp3'),
 (24, 'SALAMI_842.mp3'),
 (25, 'SALAMI_866.mp3'),
 (26, 'SALAMI_12.mp3'),
 (27, 'SALAMI_1390.mp3'),
 (28, 'SALAMI_892.mp3'),
 (29, 'SALAMI_1258.mp3'),
 (30, 'SALAMI_1314.mp3'),
 (31, 'SALAMI_1338.mp3'),
 (32, 'SALAMI_986.mp3'),
 (33, 'SALAMI_1216.mp3'),
 (34, 'SALAMI_1012.mp3'),
 (35, 'SALAMI_264.mp3'),
 (36, 'SALAMI_1406.mp3'),
 (37, 'SALAMI_18.mp3'),
 (38, 'SALAMI_1092.mp3'),
 (39, 'SALAMI_1426.mp3'),
 (40, 'SALAMI_1156.mp3'),
 (41, 'SALAMI_458.mp3'),
 (42, 'SALAMI_732.mp3'),
 (43, 'SALAMI_1418.mp3'),
 (44, 'SALAMI_834.mp3'),
 (45, 'SALAMI_798.mp3'),
 (46, 'SALAMI_856.mp3'),
 (47, 'SALAMI_1566.mp3'),
 (48, 'SALAMI_838.mp3'),
 (49, 'SALAMI_176.mp3'),
 (50, 'SALAMI_1366.mp3'),
 (51, 'SALAMI_542.mp3'),
 (52, 'SALAMI_414.mp3'),
 (53, 'SALAMI_1414.mp3'),
 (54, 'SALAMI_518.mp3'),
 (55, 'SALAMI_1540.mp3'),
 (56, 'SALAMI_316.mp3'),
 (57, 'SALAMI_592.mp3'),
 (58, 'SALAMI_920.mp3'),
 (59, 'SALAMI_528.mp3'),
 (60, 'SALAMI_656.mp3'),
 (61, 'SALAMI_446.mp3'),
 (62, 'SALAMI_678.mp3'),
 (63, 'SALAMI_768.mp3'),
 (64, 'SALAMI_1024.mp3'),
 (65, 'SALAMI_554.mp3'),
 (66, 'SALAMI_1438.mp3'),
 (67, 'SALAMI_1212.mp3'),
 (68, 'SALAMI_156.mp3'),
 (69, 'SALAMI_198.mp3'),
 (70, 'SALAMI_556.mp3'),
 (71, 'SALAMI_372.mp3'),
 (72, 'SALAMI_628.mp3'),
 (73, 'SALAMI_390.mp3'),
 (74, 'SALAMI_6.mp3'),
 (75, 'SALAMI_326.mp3'),
 (76, 'SALAMI_192.mp3'),
 (77, 'SALAMI_110.mp3'),
 (78, 'SALAMI_216.mp3'),
 (79, 'SALAMI_1266.mp3'),
 (80, 'SALAMI_802.mp3'),
 (81, 'SALAMI_1242.mp3'),
 (82, 'SALAMI_200.mp3'),
 (83, 'SALAMI_210.mp3'),
 (84, 'SALAMI_566.mp3'),
 (85, 'SALAMI_956.mp3'),
 (86, 'SALAMI_1230.mp3'),
 (87, 'SALAMI_876.mp3'),
 (88, 'SALAMI_306.mp3'),
 (89, 'SALAMI_344.mp3'),
 (90, 'SALAMI_420.mp3'),
 (91, 'SALAMI_846.mp3'),
 (92, 'SALAMI_334.mp3'),
 (93, 'SALAMI_250.mp3'),
 (94, 'SALAMI_1542.mp3'),
 (95, 'SALAMI_568.mp3'),
 (96, 'SALAMI_812.mp3'),
 (97, 'SALAMI_1206.mp3'),
 (98, 'SALAMI_1336.mp3'),
 (99, 'SALAMI_734.mp3'),
 (100, 'SALAMI_904.mp3'),
 (101, 'SALAMI_1244.mp3'),
 (102, 'SALAMI_600.mp3'),
 (103, 'SALAMI_570.mp3'),
 (104, 'SALAMI_1416.mp3'),
 (105, 'SALAMI_872.mp3'),
 (106, 'SALAMI_408.mp3'),
 (107, 'SALAMI_882.mp3'),
 (108, 'SALAMI_496.mp3'),
 (109, 'SALAMI_668.mp3'),
 (110, 'SALAMI_738.mp3'),
 (111, 'SALAMI_454.mp3'),
 (112, 'SALAMI_770.mp3'),
 (113, 'SALAMI_830.mp3'),
 (114, 'SALAMI_1288.mp3'),
 (115, 'SALAMI_168.mp3'),
 (116, 'SALAMI_1166.mp3'),
 (117, 'SALAMI_1484.mp3'),
 (118, 'SALAMI_1160.mp3'),
 (119, 'SALAMI_1506.mp3'),
 (120, 'SALAMI_346.mp3'),
 (121, 'SALAMI_1158.mp3'),
 (122, 'SALAMI_608.mp3'),
 (123, 'SALAMI_188.mp3'),
 (124, 'SALAMI_636.mp3'),
 (125, 'SALAMI_270.mp3'),
 (126, 'SALAMI_120.mp3'),
 (127, 'SALAMI_916.mp3'),
 (128, 'SALAMI_966.mp3'),
 (129, 'SALAMI_674.mp3'),
 (130, 'SALAMI_704.mp3'),
 (131, 'SALAMI_1178.mp3'),
 (132, 'SALAMI_436.mp3'),
 (133, 'SALAMI_1082.mp3'),
 (134, 'SALAMI_336.mp3'),
 (135, 'SALAMI_1432.mp3'),
 (136, 'SALAMI_932.mp3'),
 (137, 'SALAMI_938.mp3'),
 (138, 'SALAMI_1498.mp3'),
 (139, 'SALAMI_108.mp3'),
 (140, 'SALAMI_1032.mp3'),
 (141, 'SALAMI_1060.mp3'),
 (142, 'SALAMI_806.mp3'),
 (143, 'SALAMI_1344.mp3'),
 (144, 'SALAMI_816.mp3'),
 (145, 'SALAMI_520.mp3'),
 (146, 'SALAMI_578.mp3'),
 (147, 'SALAMI_1002.mp3'),
 (148, 'SALAMI_1530.mp3'),
 (149, 'SALAMI_380.mp3'),
 (150, 'SALAMI_1310.mp3'),
 (151, 'SALAMI_1342.mp3'),
 (152, 'SALAMI_516.mp3'),
 (153, 'SALAMI_482.mp3'),
 (154, 'SALAMI_1358.mp3'),
 (155, 'SALAMI_1010.mp3'),
 (156, 'SALAMI_562.mp3'),
 (157, 'SALAMI_72.mp3'),
 (158, 'SALAMI_1308.mp3'),
 (159, 'SALAMI_1452.mp3'),
 (160, 'SALAMI_386.mp3'),
 (161, 'SALAMI_1038.mp3'),
 (162, 'SALAMI_1202.mp3'),
 (163, 'SALAMI_886.mp3'),
 (164, 'SALAMI_690.mp3'),
 (165, 'SALAMI_360.mp3'),
 (166, 'SALAMI_1208.mp3'),
 (167, 'SALAMI_1186.mp3'),
 (168, 'SALAMI_376.mp3'),
 (169, 'SALAMI_456.mp3'),
 (170, 'SALAMI_536.mp3'),
 (171, 'SALAMI_88.mp3'),
 (172, 'SALAMI_132.mp3'),
 (173, 'SALAMI_728.mp3'),
 (174, 'SALAMI_1104.mp3'),
 (175, 'SALAMI_676.mp3'),
 (176, 'SALAMI_1124.mp3'),
 (177, 'SALAMI_1232.mp3'),
 (178, 'SALAMI_1514.mp3'),
 (179, 'SALAMI_42.mp3'),
 (180, 'SALAMI_294.mp3'),
 (181, 'SALAMI_122.mp3'),
 (182, 'SALAMI_4.mp3'),
 (183, 'SALAMI_544.mp3'),
 (184, 'SALAMI_780.mp3'),
 (185, 'SALAMI_44.mp3'),
 (186, 'SALAMI_612.mp3'),
 (187, 'SALAMI_338.mp3'),
 (188, 'SALAMI_1058.mp3'),
 (189, 'SALAMI_52.mp3'),
 (190, 'SALAMI_688.mp3'),
 (191, 'SALAMI_666.mp3'),
 (192, 'SALAMI_92.mp3'),
 (193, 'SALAMI_788.mp3'),
 (194, 'SALAMI_166.mp3'),
 (195, 'SALAMI_46.mp3'),
 (196, 'SALAMI_8.mp3'),
 (197, 'SALAMI_1408.mp3'),
 (198, 'SALAMI_1582.mp3'),
 (199, 'SALAMI_526.mp3'),
 (200, 'SALAMI_1492.mp3'),
 (201, 'SALAMI_586.mp3'),
 (202, 'SALAMI_928.mp3'),
 (203, 'SALAMI_152.mp3'),
 (204, 'SALAMI_640.mp3'),
 (205, 'SALAMI_778.mp3'),
 (206, 'SALAMI_1410.mp3'),
 (207, 'SALAMI_854.mp3'),
 (208, 'SALAMI_1302.mp3'),
 (209, 'SALAMI_1356.mp3'),
 (210, 'SALAMI_242.mp3'),
 (211, 'SALAMI_1422.mp3'),
 (212, 'SALAMI_1030.mp3'),
 (213, 'SALAMI_1368.mp3'),
 (214, 'SALAMI_1436.mp3'),
 (215, 'SALAMI_1486.mp3'),
 (216, 'SALAMI_1450.mp3'),
 (217, 'SALAMI_240.mp3'),
 (218, 'SALAMI_936.mp3'),
 (219, 'SALAMI_1320.mp3'),
 (220, 'SALAMI_480.mp3'),
 (221, 'SALAMI_1596.mp3'),
 (222, 'SALAMI_902.mp3'),
 (223, 'SALAMI_850.mp3'),
 (224, 'SALAMI_1440.mp3'),
 (225, 'SALAMI_148.mp3'),
 (226, 'SALAMI_944.mp3'),
 (227, 'SALAMI_350.mp3'),
 (228, 'SALAMI_1424.mp3'),
 (229, 'SALAMI_100.mp3'),
 (230, 'SALAMI_922.mp3'),
 (231, 'SALAMI_1228.mp3'),
 (232, 'SALAMI_1088.mp3'),
 (233, 'SALAMI_1474.mp3'),
 (234, 'SALAMI_90.mp3'),
 (235, 'SALAMI_1260.mp3'),
 (236, 'SALAMI_994.mp3'),
 (237, 'SALAMI_1490.mp3'),
 (238, 'SALAMI_724.mp3'),
 (239, 'SALAMI_370.mp3'),
 (240, 'SALAMI_1568.mp3'),
 (241, 'SALAMI_1384.mp3'),
 (242, 'SALAMI_368.mp3'),
 (243, 'SALAMI_808.mp3'),
 (244, 'SALAMI_124.mp3'),
 (245, 'SALAMI_1550.mp3'),
 (246, 'SALAMI_394.mp3'),
 (247, 'SALAMI_1296.mp3'),
 (248, 'SALAMI_1312.mp3'),
 (249, 'SALAMI_848.mp3'),
 (250, 'SALAMI_328.mp3'),
 (251, 'SALAMI_1210.mp3'),
 (252, 'SALAMI_332.mp3'),
 (253, 'SALAMI_1008.mp3'),
 (254, 'SALAMI_758.mp3'),
 (255, 'SALAMI_460.mp3'),
 (256, 'SALAMI_1248.mp3'),
 (257, 'SALAMI_604.mp3'),
 (258, 'SALAMI_364.mp3'),
 (259, 'SALAMI_1014.mp3'),
 (260, 'SALAMI_82.mp3'),
 (261, 'SALAMI_686.mp3'),
 (262, 'SALAMI_1246.mp3'),
 (263, 'SALAMI_274.mp3'),
 (264, 'SALAMI_894.mp3'),
 (265, 'SALAMI_1072.mp3'),
 (266, 'SALAMI_228.mp3'),
 (267, 'SALAMI_852.mp3'),
 (268, 'SALAMI_1364.mp3'),
 (269, 'SALAMI_648.mp3'),
 (270, 'SALAMI_410.mp3'),
 (271, 'SALAMI_1374.mp3'),
 (272, 'SALAMI_398.mp3'),
 (273, 'SALAMI_652.mp3'),
 (274, 'SALAMI_1168.mp3'),
 (275, 'SALAMI_828.mp3'),
 (276, 'SALAMI_950.mp3'),
 (277, 'SALAMI_930.mp3'),
 (278, 'SALAMI_712.mp3'),
 (279, 'SALAMI_1270.mp3'),
 (280, 'SALAMI_340.mp3'),
 (281, 'SALAMI_696.mp3'),
 (282, 'SALAMI_86.mp3'),
 (283, 'SALAMI_888.mp3'),
 (284, 'SALAMI_1192.mp3'),
 (285, 'SALAMI_1106.mp3'),
 (286, 'SALAMI_794.mp3'),
 (287, 'SALAMI_94.mp3'),
 (288, 'SALAMI_1164.mp3'),
 (289, 'SALAMI_1434.mp3'),
 (290, 'SALAMI_700.mp3'),
 (291, 'SALAMI_1214.mp3'),
 (292, 'SALAMI_214.mp3'),
 (293, 'SALAMI_800.mp3'),
 (294, 'SALAMI_470.mp3'),
 (295, 'SALAMI_1252.mp3'),
 (296, 'SALAMI_260.mp3'),
 (297, 'SALAMI_1532.mp3'),
 (298, 'SALAMI_1262.mp3'),
 (299, 'SALAMI_348.mp3'),
 (300, 'SALAMI_1554.mp3'),
 (301, 'SALAMI_1592.mp3'),
 (302, 'SALAMI_218.mp3'),
 (303, 'SALAMI_1456.mp3'),
 (304, 'SALAMI_1278.mp3'),
 (305, 'SALAMI_860.mp3'),
 (306, 'SALAMI_58.mp3'),
 (307, 'SALAMI_670.mp3'),
 (308, 'SALAMI_1458.mp3'),
 (309, 'SALAMI_924.mp3'),
 (310, 'SALAMI_1250.mp3'),
 (311, 'SALAMI_388.mp3'),
 (312, 'SALAMI_864.mp3'),
 (313, 'SALAMI_1386.mp3'),
 (314, 'SALAMI_1558.mp3'),
 (315, 'SALAMI_1524.mp3'),
 (316, 'SALAMI_128.mp3'),
 (317, 'SALAMI_1504.mp3'),
 (318, 'SALAMI_422.mp3'),
 (319, 'SALAMI_874.mp3'),
 (320, 'SALAMI_514.mp3'),
 (321, 'SALAMI_832.mp3'),
 (322, 'SALAMI_558.mp3'),
 (323, 'SALAMI_680.mp3'),
 (324, 'SALAMI_1348.mp3'),
 (325, 'SALAMI_718.mp3'),
 (326, 'SALAMI_1102.mp3'),
 (327, 'SALAMI_1268.mp3'),
 (328, 'SALAMI_1598.mp3'),
 (329, 'SALAMI_1522.mp3'),
 (330, 'SALAMI_1404.mp3'),
 (331, 'SALAMI_954.mp3'),
 (332, 'SALAMI_170.mp3'),
 (333, 'SALAMI_320.mp3'),
 (334, 'SALAMI_1510.mp3'),
 (335, 'SALAMI_1588.mp3'),
 (336, 'SALAMI_1118.mp3'),
 (337, 'SALAMI_1520.mp3'),
 (338, 'SALAMI_1306.mp3'),
 (339, 'SALAMI_610.mp3'),
 (340, 'SALAMI_532.mp3'),
 (341, 'SALAMI_382.mp3'),
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 (766, 'SALAMI_268.mp3'),
 (767, 'SALAMI_978.mp3'),
 (768, 'SALAMI_472.mp3')]

















In [329]:



    


k = 40



    











In [330]:



    


S_p, S, intframes = plot_selfsim(max_samples=1000, **all_data['file_scores'][k])



    











In [331]:



    


np.set_printoptions(precision=0, linewidth=120)



    











In [332]:



    


all_data['file_scores'][k]['thresholds']['none']



    


















    
Out[332]:








36.89056

















In [333]:



    


all_data['file_scores'][k]['scores'] <= all_data['file_scores'][k]['thresholds']['none']



    


















    
Out[333]:








array([[ True, False, False, False, False, False, False, False, False, False, False, False],
       [False,  True,  True,  True,  True, False, False,  True,  True,  True, False, False],
       [False,  True,  True,  True,  True, False, False,  True,  True,  True, False, False],
       [False,  True,  True,  True,  True, False, False,  True,  True,  True, False, False],
       [False,  True,  True,  True,  True, False, False,  True,  True,  True, False, False],
       [False, False, False, False, False,  True, False, False, False, False, False, False],
       [False, False, False, False, False, False,  True, False, False, False, False, False],
       [False,  True,  True,  True,  True, False, False,  True,  True,  True, False, False],
       [False,  True,  True,  True,  True, False, False,  True,  True,  True, False, False],
       [False,  True,  True,  True,  True, False, False,  True,  True,  True, False, False],
       [False, False, False, False, False, False, False, False, False, False,  True, False],
       [False, False, False, False, False, False, False, False, False, False, False,  True]], dtype=bool)

















In [334]:



    


import seaborn as sns



    











In [335]:



    


sns.heatmap(all_data['file_scores'][k]['scores'] <= all_data['file_scores'][k]['thresholds']['none'],
            yticklabels=all_data['file_scores'][k]['labels'])



    


















    
Out[335]:








<matplotlib.axes._subplots.AxesSubplot at 0x7ffa071ab210>










    
























In [336]:



    


plt.figure(figsize=(12,8))
plt.subplot(1,2,1)
librosa.display.specshow(S, aspect='equal', origin='upper')
plt.vlines(intframes[:, 0], 0, S.shape[0], color='w', alpha=0.5)
plt.hlines(intframes[:, 0], 0, S.shape[0], color='w', alpha=0.5)
plt.subplot(1,2,2)
librosa.display.specshow(S_p + np.eye(len(S_p)), vmin=-1, vmax=1.0, cmap='coolwarm', aspect='equal', origin='upper',
                         interpolation='bilinear')
plt.vlines(intframes[:, 0], 0, S.shape[0], color='k', alpha=0.5)
plt.hlines(intframes[:, 0], 0, S.shape[0], color='k', alpha=0.5)
plt.tight_layout()



    


















    
























In [337]:



    


Parallel(n_jobs=4, verbose=20)(delayed(process_file)(data) for data in all_data['file_scores']);



    


















    






[Parallel(n_jobs=4)]: Done   1 out of 769 | elapsed:    0.6s remaining:  8.2min
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Content source: urinieto/similarity_classification

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