音乐流派分类

1.https://www.jiqizhixin.com/articles/2019-01-11-25（讲解）

2.https://gist.github.com/parulnith/7f8c174e6ac099e86f0495d3d9a4c01e#file-music_genre_classification-ipynb（源码）

# feature extractoring and preprocessing data
import librosa
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
from PIL import Image
import pathlib
import csv
# Preprocessing
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
#Keras
import keras
import warnings
from keras import models
from keras import layers
from keras.models import load_model

warnings.filterwarnings('ignore')
cmap = plt.get_cmap('inferno')
plt.figure(figsize=(10,10))
# genres = 'blues classical country disco hiphop jazz metal pop reggae rock'.split()
# #转换成对应的谱图，保存到imag_data文件夹里面
# for g in genres:
#     pathlib.Path(f'img_data/{g}').mkdir(parents=True, exist_ok=True)     
#     for filename in os.listdir(f'./music/{g}'):
#         songname = f'./music/{g}/{filename}'
#         y, sr = librosa.load(songname, mono=True, duration=5)
#         plt.specgram(y, NFFT=2048, Fs=2, Fc=0, noverlap=128, cmap=cmap, sides='default', mode='default', scale='dB');
#         plt.axis('off')
#         plt.savefig(f'img_data/{g}/{filename[:-3].replace(".", "")}.png')
#         plt.clf()



 #提取各个音频的特征
# header = 'filename chroma_stft rmse spectral_centroid spectral_bandwidth rolloff zero_crossing_rate'
# for i in range(1, 21):
#     header += f' mfcc{i}'
# header += ' label'
# header = header.split()


# file = open('data.csv', 'w', newline='')
# with file:
#     writer = csv.writer(file)
#     writer.writerow(header)
# genres = 'blues classical country disco hiphop jazz metal pop reggae rock'.split()
# for g in genres:
#     for filename in os.listdir(f'./music/{g}'):
#         songname = f'./music/{g}/{filename}'
#         y, sr = librosa.load(songname, mono=True, duration=30)
#         chroma_stft = librosa.feature.chroma_stft(y=y, sr=sr)
#         rmse=librosa.feature.rms(y=y)
#         spec_cent = librosa.feature.spectral_centroid(y=y, sr=sr)
#         spec_bw = librosa.feature.spectral_bandwidth(y=y, sr=sr)
#         rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr)
#         zcr = librosa.feature.zero_crossing_rate(y)
#         mfcc = librosa.feature.mfcc(y=y, sr=sr)
#         to_append = f'{filename} {np.mean(chroma_stft)} {np.mean(rmse)} {np.mean(spec_cent)} {np.mean(spec_bw)} {np.mean(rolloff)} {np.mean(zcr)}'    
#         for e in mfcc:
#             to_append += f' {np.mean(e)}'
#         to_append += f' {g}'
#         file = open('data.csv', 'a', newline='')
#         with file:
#             writer = csv.writer(file)
#             writer.writerow(to_append.split())


#用keras训练模型
data = pd.read_csv('data.csv')
genre_list = data.iloc[:, -1]
encoder = LabelEncoder()
#将标签y进行数字化表示（0-9）
y = encoder.fit_transform(genre_list)
scaler = StandardScaler()
#标准化数据特征
X = scaler.fit_transform(np.array(data.iloc[:, 1:-1], dtype = float))
#切分数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# model = models.Sequential()
# model.add(layers.Dense(256, activation='relu', input_shape=(X_train.shape[1],)))
# model.add(layers.Dense(128, activation='relu'))
# model.add(layers.Dense(64, activation='relu'))
# model.add(layers.Dense(10, activation='softmax'))
# model.compile(optimizer='adam',
#               loss='sparse_categorical_crossentropy',
#               metrics=['accuracy'])
# history = model.fit(X_train,
#                     y_train,
#                     epochs=20,
#                     batch_size=128)
# test_loss, test_acc = model.evaluate(X_test,y_test)
# print()
# print('test_acc: ',test_acc)
# print('test_loss: ',test_loss)
# model.save('music_model.h5')
model = load_model('music_model.h5')

#验证：
predictions = model.predict(X_test)
acc=0
sum=len(predictions)
for i in range(len(predictions)):
    if(np.argmax(predictions[i])==y_test[i]):
        acc=acc+1
    print("预测：",np.argmax(predictions[0]),"真实：",y_test[i])
print("正确率：",acc/sum)