4.45语音分类

依赖包导入

# 基本库
import pandas as pd
import numpy as np

from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold,StratifiedKFold

from sklearn.metrics import classification_report
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score

from sklearn.preprocessing import minmax_scale
# 搭建分类模型所需要的库

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, Flatten, Dense, MaxPool2D, Dropout, LSTM, BatchNormalization
from tensorflow.keras.utils import to_categorical 
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
import os
import librosa
import librosa.display
import glob 
from tqdm import tqdm
from tensorflow.keras.callbacks import ReduceLROnPlateau
from keras import optimizers
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.ensemble import VotingClassifier
from keras import regularizers

from sklearn.utils import class_weight

数据分析与处理`

feature = []
label = []
# 建立类别标签，不同类别对应不同的数字。
label_dict = {'aloe': 0, 'burger': 1, 'cabbage': 2,'candied_fruits':3, 'carrots': 4, 'chips':5,
                  'chocolate': 6, 'drinks': 7, 'fries': 8, 'grapes': 9, 'gummies': 10, 'ice-cream':11,
                  'jelly': 12, 'noodles': 13, 'pickles': 14, 'pizza': 15, 'ribs': 16, 'salmon':17,
                  'soup': 18, 'wings': 19}
label_dict_inv = {v:k for k,v in label_dict.items()}

new_parent_dir = './soxed_train/'
old_parent_dir = './train/'
save_dir = "./"
folds = sub_dirs = np.array(['aloe','burger','cabbage','candied_fruits',
                             'carrots','chips','chocolate','drinks','fries',
                            'grapes','gummies','ice-cream','jelly','noodles','pickles',
                            'pizza','ribs','salmon','soup','wings'])
def sox_wavs(new_parent_dir, old_parent_dir, sub_dirs, max_file=1, file_ext="*.wav"):

    for sub_dir in sub_dirs:
        if not os.path.exists(os.path.join(new_parent_dir, sub_dir)):
            os.makedirs(os.path.join(new_parent_dir, sub_dir))
        for fn in tqdm(glob.glob(os.path.join(old_parent_dir, sub_dir, file_ext))[:max_file]): # 遍历数据集的所有文件
            new_fn = fn.replace(old_parent_dir, new_parent_dir)
#             print(f'sox -b 16 -e signed-integer {fn} {new_fn}')
            os.system(f'sox -b 16 -e signed-integer {fn} {new_fn}')
sox_wavs(new_parent_dir, old_parent_dir, sub_dirs, max_file=10000, file_ext="*.wav")

#抽取单样特征
def extract_mfcc(parent_dir, sub_dirs, max_file=10, file_ext="*.wav"):
    label, feature = [], []
    for sub_dir in sub_dirs:
        for fn in tqdm(glob.glob(os.path.join(parent_dir, sub_dir, file_ext))[:max_file]): # 遍历数据集的所有文件
            label_name = fn.split('\')[-2]
            label.extend([label_dict[label_name]])
            X, sample_rate = librosa.load(fn,res_type='kaiser_fast')
            mfcc = librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=128).T 
            feature.append(mfcc)
            
    return [feature, label]
# 获取特征feature以及类别的label

# 自己更改目录
parent_dir = './clips_rd_sox/'
save_dir = "./"
folds = sub_dirs = np.array(['aloe','burger','cabbage','candied_fruits',
                             'carrots','chips','chocolate','drinks','fries',
                            'grapes','gummies','ice-cream','jelly','noodles','pickles',
                            'pizza','ribs','salmon','soup','wings'])

mfcc_128_all,label = extract_mfcc(parent_dir,sub_dirs,max_file=1000)

不论是选用梅尔频谱还是梅尔倒谱，对于整体来讲，分数都是有所提升的，而这里选用的128过滤器，相比于baseline中的20，效果更好了，而在这之前的sox是linux中用于规整频率的一种工具，可能对于音频研究者来讲，会比较熟悉。
模型构建

CNN

nfold = 5
kf = KFold(n_splits=nfold, shuffle=True, random_state=2020)
prediction1 = np.zeros((2000,20 ))
# print(prediction1.shape)
i = 0
for train_index, valid_index in kf.split(X, Y):
    print("nFold {}".format(i + 1))
    train_x, val_x = X[train_index],X[valid_index]
    train_y, val_y = Y[train_index],Y[valid_index]
    train_x = train_x.reshape(-1, 16, 8, 1)
    val_x = val_x.reshape(-1, 16, 8, 1)
    # print(train_x.shape)
    # print(val_x.shape)
    train_y = to_categorical(train_y)
    val_y = to_categorical(val_y)
    # print(train_y.shape)
    # print(val_y.shape)
    model = Sequential()
    model.add(Convolution2D(64, (5, 5),padding = "same", input_shape=input_dim, activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Convolution2D(128, (3, 3),padding = "same",activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.1))
    model.add(Flatten())
    model.add(Dense(1024, activation='relu'))
    model.add(Dense(100, activation='relu'))
    model.add(Dense(20, activation='softmax'))
    model.compile(optimizer='Adam',
                    loss='categorical_crossentropy',
                    metrics=['accuracy'])
    model.summary()
    history = model.fit(train_x, train_y, epochs = 120, batch_size = 128, validation_data = (val_x, val_y))
    # X_test = np.vstack(X_test)
    predictions = model.predict(X_test.reshape(-1, 16, 8, 1))
    print(predictions.shape)
    prediction1 += ((predictions)) / nfold
    i += 1

DNN

# 定义模型
def built_model():
    model_dense = Sequential()

    model_dense.add(Dense(128, activation='relu', kernel_regularizer=regularizers.l2(0.005)))
    model_dense.add(BatchNormalization())
    model_dense.add(Dropout(0.2))
    model_dense.add(Dense(256, activation='relu', kernel_regularizer=regularizers.l2(0.005)))
    model_dense.add(BatchNormalization())
    model_dense.add(Dropout(0.3))
    model_dense.add(Dense(512, activation='relu',kernel_regularizer=regularizers.l2(0.005)))
    model_dense.add(BatchNormalization())
    model_dense.add(Dropout(0.3))
    model_dense.add(Dense(256, activation='relu', kernel_regularizer=regularizers.l2(0.001)))
    model_dense.add(BatchNormalization())
    model_dense.add(Dropout(0.2))
    model_dense.add(Dense(20, activation='softmax')) # 输出层：20个units输出20个类的概率
    # 编译模型，设置损失函数，优化方法以及评价标准
    optimizer = optimizers.Adam(learning_rate=0.001)
    model_dense.compile(optimizer = optimizer, loss = 'categorical_crossentropy', metrics = ['accuracy'], )
    return model_dense

checkpoint = ModelCheckpoint(filepath, monitor='val_accuracy', verbose=1, save_best_only=True, mode='max', period=1)
reduce_lr = ReduceLROnPlateau(monitor='val_accuracy', patience=20, mode='auto', factor=0.8 )
EarlyStop = EarlyStopping(monitor='val_accuracy', patience=200, verbose=1, mode='max')

lightgbm

def cv_model(clf, train_x, train_y, test_x, clf_name):
    folds = 10
    seed = 2020
    kf = StratifiedKFold(n_splits=folds, shuffle=True, random_state=seed )

    train = np.zeros(train_x.shape[0])
    test = np.zeros((700,20))
    
    cv_scores = []
    
    print(train_y.shape)
    for i, (train_index, valid_index) in enumerate(kf.split(train_x, train_y)):
        print('************************************ {} ************************************'.format(str(i+1)))
        trn_x, trn_y, val_x, val_y = train_x[train_index], train_y[train_index], train_x[valid_index], train_y[valid_index]
        
        if clf_name == "lgb":
            
            train_matrix = clf.Dataset(trn_x, label=trn_y)
            valid_matrix = clf.Dataset(val_x, label=val_y)
            
            params = {
                'boosting_type': 'gbdt',
                'objective': 'multiclass',
                'metric': 'multi_error',
                'min_child_weight': 5,
                'num_leaves': 2 ** 4,
                'lambda_l2': 13,
                'feature_fraction': 0.6,
                'bagging_fraction': 0.7,
                'bagging_freq': 2,
                'learning_rate': 0.1,
                'seed': 2020,
                'nthread': 24,
                'n_jobs':24,
                'silent': True,
                'verbose': -1,
                'num_class':20,
            }

            model = clf.train(params, train_matrix, 50000, valid_sets=[train_matrix, valid_matrix], verbose_eval=200,early_stopping_rounds=200)
            val_pred = model.predict(val_x, num_iteration=model.best_iteration)
            test_pred = model.predict(test_x, num_iteration=model.best_iteration)
            
            # print(list(sorted(zip(features, model.feature_importance("gain")), key=lambda x: x[1], reverse=True))[:20])
                    
#         train[valid_index] += val_pred
#         print(test_pred.shape)
        test += test_pred / kf.n_splits
#         print(val_pred.shape)
        cv_scores.append(accuracy_score(val_y, np.argmax(val_pred, axis=1)))
        
#         print(cv_scores)
        
    print("%s_scotrainre_list:" % clf_name, cv_scores)
    print("%s_score_mean:" % clf_name, np.mean(cv_scores))
    print("%s_score_std:" % clf_name, np.std(cv_scores))
    return train, test

def lgb_model(x_train, y_train, x_test):
    lgb_train, lgb_test = cv_model(lgb, x_train, y_train, x_test, "lgb")
    return lgb_train, lgb_test

模型融合
根据反馈，lightgbm调调参数能到94左右，而DNN和CNN都有97+的成绩，LSTM的效果看起来不行，可能这数据还是不太具有时序性，以原数据sox后128滤波，两个DNN和一CNN进行vote，能到99+.

# 投票
def voting(preds_conv, preds_dense, preds_lstm):
    prob_max = np.tile(np.max(preds_conv, axis=1).reshape(-1, 1), preds_conv.shape[1])
    preds_c = preds_conv // prob_max
    prob_max = np.tile(np.max(preds_dense, axis=1).reshape(-1, 1), preds_dense.shape[1])
    preds_d = preds_dense // prob_max
    prob_max = np.tile(np.max(preds_lstm, axis=1).reshape(-1, 1), preds_lstm.shape[1])
    preds_l = preds_lstm // prob_max
    result_voting = preds_c + preds_d + preds_l
    preds_voting = np.argmax(result_voting, axis=1)
    return preds_voting
preds_voting = voting(preds_conv, preds_dense, preds_dense)