average_distance_va, nearest_dist_average_va, no_stdev_average_va, find_in_dict, valence_distance_va, arousal_distance_va) from utils.plot import (plot_all_va) import numpy as np import random import sys # y, sr = load_files('audio/101.mp3') # mfcc_v = mfcc(y, sr) # get exsisting valence and arousal data valence, arousal = csv_2_dict_va('csv/survery2dataMin1.csv') # calculate fetures for song in train set ids, feat = read_fake_chroma('features/fakechroma') best_avg = sys.maxint best_near = sys.maxint best_std = sys.maxint best_val = sys.maxint best_aro = sys.maxint for i in range(50): train_ids = ids random.shuffle(train_ids) all_ids = train_ids[141:] train_ids = train_ids[0:140] # calcultae valence and arousal find_a_v_mens
) from utils.plot import( plot_all_va ) import numpy as np import random # y, sr = load_files('audio/101.mp3') # mfcc_v = mfcc(y, sr) # get exsisting valence and arousal data valence, arousal = csv_2_dict_va('csv/survery2dataMin1.csv') # calculate fetures for song in train set ids, feat = read_fake_chroma('features/spectrum') train_ids = ids random.shuffle(train_ids) all_ids = train_ids[141:] train_ids = train_ids[0:140] # calcultae valence and arousal find_a_v_mens val_mean, aro_mean = find_a_v_mens_va(train_ids, valence, arousal) train_feat = find_in_dict(feat, train_ids) test_feat = find_in_dict(feat, all_ids) # use regression X_v, X_a = regression(train_feat, val_mean, aro_mean) # calculating features for whole dataset
from utils.plot import( plot_all_va ) import numpy as np import random import sys # y, sr = load_files('audio/101.mp3') # mfcc_v = mfcc(y, sr) # get exsisting valence and arousal data valence, arousal = csv_2_dict_va('csv/survery2dataMin1.csv') # calculate fetures for song in train set ids, feat = read_fake_chroma('features/fakechroma') best_avg = sys.maxint best_near = sys.maxint best_std = sys.maxint best_val = sys.maxint best_aro = sys.maxint for i in range(50): train_ids = ids random.shuffle(train_ids) all_ids = train_ids[141:] train_ids = train_ids[0:140]
from utils.calc_utils import (find_a_v_mens_va, regression, average_distance_va, nearest_dist_average_va, no_stdev_average_va, find_in_dict) from utils.plot import (plot_all_va) import numpy as np import random # y, sr = load_files('audio/101.mp3') # mfcc_v = mfcc(y, sr) # get exsisting valence and arousal data valence, arousal = csv_2_dict_va('csv/survery2dataMin1.csv') # calculate fetures for song in train set ids, feat = read_fake_chroma('features/spectrum') train_ids = ids random.shuffle(train_ids) all_ids = train_ids[141:] train_ids = train_ids[0:140] # calcultae valence and arousal find_a_v_mens val_mean, aro_mean = find_a_v_mens_va(train_ids, valence, arousal) train_feat = find_in_dict(feat, train_ids) test_feat = find_in_dict(feat, all_ids) # use regression X_v, X_a = regression(train_feat, val_mean, aro_mean) # calculating features for whole dataset