pyln.normalize.loudness
时间: 2023-09-05 21:11:10 浏览: 157
pyln.normalize.loudness是Python中pyloudnorm库中的一个函数,用于将音频信号的整体响度调整到指定的目标响度。
其参数如下:
- audio: 输入的音频信号
- loudness_target: 目标响度值。默认值为-24.0 LUFS
- peak: 音频信号的峰值。默认值为None,表示使用最大值作为峰值。
函数的返回值为一个元组,包含两个值:
- 输出的音频信号
- 输出的响度值
该函数使用了ITU-R BS.1770-4标准,通过计算音频信号的整体响度,将其调整到目标响度值。同时,该函数也会调整音频信号的增益,使其峰值不超过指定的peak值。
相关问题
transforms.Normalize
`transforms.Normalize`是PyTorch中的一个数据转换函数,它用于将数据规范化(标准化)为均值为0,标准差为1的数据。这通常在深度学习中很有用,因为它可以帮助模型更快地收敛和提高模型的准确性。
`transforms.Normalize`的输入参数是均值和标准差,分别对应于每个通道的均值和标准差。例如,对于RGB图片,它需要3个均值和3个标准差。它可以用在数据预处理的管道中,例如在`transforms.Compose`中。
以下是一个示例,展示了如何使用`transforms.Normalize`,其中`mean`和`std`分别是每个通道的均值和标准差。
```python
import torchvision.transforms as transforms
# 定义均值和标准差
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
# 定义预处理管道
transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std)
])
```
在上述预处理管道中,输入图像首先被调整大小为256x256像素,然后从中心裁剪成224x224像素,然后转换为张量,最后使用`transforms.Normalize`进行规范化。
transforms.normalize
`transforms.normalize()` is a method in the PyTorch library's `transforms` module that is used to normalize a tensor image with mean and standard deviation.
It takes two arguments: mean and standard deviation. These values are usually pre-calculated for the dataset that the tensor image belongs to. The method then normalizes each channel of the tensor image by subtracting the mean and dividing by the standard deviation.
Here is an example of how to use `transforms.normalize()`:
```
import torchvision.transforms as transforms
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
```
In this example, we are creating a `transform` object that first converts a PIL image to a tensor using `transforms.ToTensor()`, and then normalizes the tensor image using `transforms.Normalize()` with the mean and standard deviation values specified. This transform can then be applied to a dataset using a data loader to prepare the data for a machine learning model.