用梅尔倒频谱乐器识别代码
时间: 2023-09-29 09:07:21 浏览: 45
以下是基于梅尔倒频谱系数(Mel-frequency cepstral coefficients,MFCC)的乐器识别代码框架:
1. 导入必要的库和数据集
```
import os
import librosa
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import tensorflow as tf
from tensorflow.keras import layers
# 定义数据集路径
data_dir = '/path/to/dataset'
```
2. 定义提取MFCC特征的函数
```
def extract_features(file_path):
# 读取音频文件
signal, sr = librosa.load(file_path, sr=22050)
# 提取MFCC特征
mfccs = librosa.feature.mfcc(signal, sr=sr, n_mfcc=13)
mfccs = np.mean(mfccs.T, axis=0)
return mfccs
```
3. 加载数据集并提取MFCC特征
```
# 加载数据集
data = pd.read_csv(os.path.join(data_dir, 'data.csv'))
# 提取MFCC特征
features = []
labels = []
for i, row in data.iterrows():
file_path = os.path.join(data_dir, row['filename'])
class_label = row['label']
feature = extract_features(file_path)
features.append(feature)
labels.append(class_label)
# 将标签进行编码
le = LabelEncoder()
labels = le.fit_transform(labels)
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)
# 将数据转换为Numpy数组并进行标准化
X_train = np.array(X_train)
X_test = np.array(X_test)
y_train = np.array(y_train)
y_test = np.array(y_test)
mean = np.mean(X_train, axis=0)
std = np.std(X_train, axis=0)
X_train = (X_train - mean) / std
X_test = (X_test - mean) / std
```
4. 定义模型并进行训练
```
model = tf.keras.Sequential([
layers.Dense(256, activation='relu', input_shape=(13,)),
layers.Dropout(0.3),
layers.Dense(128, activation='relu'),
layers.Dropout(0.3),
layers.Dense(64, activation='relu'),
layers.Dropout(0.3),
layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=100, batch_size=32, validation_data=(X_test, y_test))
```
5. 模型评估和预测
```
# 在测试集上评估模型
test_loss, test_acc = model.evaluate(X_test, y_test)
print('Test accuracy:', test_acc)
# 进行预测
predictions = model.predict(X_test)
predicted_labels = np.argmax(predictions, axis=1)
# 将预测结果进行解码
decoded_predicted_labels = le.inverse_transform(predicted_labels)
```
以上是基于MFCC的乐器识别代码框架,可以根据自己的需求进行修改和优化。
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