Python read_fake_chroma Beispiele

Beispiel #1

Datei: va_with_matevz_chroma.py Projekt: hainakus/MoodViaMFCCRegression

                              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

Beispiel #2

Datei: va_with_matevz_fulvec.py Projekt: PrimozGodec/MoodViaMFCCRegression

)
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

Beispiel #3

Datei: va_with_matevz_chroma.py Projekt: PrimozGodec/MoodViaMFCCRegression

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]

Beispiel #4

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