# helia_edge.layers.preprocessing.random_gaussian_noise

## Random Gaussian Noise Layer API

This module provides classes to build random Gaussian noise layers.

**Classes**

| Name | Description |
| --- | --- |
| `RandomGaussianNoise1D` | Random Gaussian noise 1D |

## helia_edge.layers.preprocessing.random_gaussian_noise.RandomGaussianNoise1D

`class` · `python`

```python
RandomGaussianNoise1D(factor: float | tuple[float, float] = 0.1, **kwargs={})
```

Apply additive zero-centered Gaussian noise.

Example:

```python
    x = np.sin(2*np.pi*10*np.arange(duration_size)/100)
    lyr = RandomGaussianNoise1D(factor=0.1)
    y = lyr(x)
```



Base class: [BaseAugmentation1D](/helia-edge/reference/api/helia_edge/layers/preprocessing/base_augmentation/#helia_edge.layers.preprocessing.base_augmentation.BaseAugmentation1D).

Inherited from [BaseAugmentation](/helia-edge/reference/api/helia_edge/layers/preprocessing/base_augmentation/#helia_edge.layers.preprocessing.base_augmentation.BaseAugmentation): [augment_masks()](/helia-edge/reference/api/helia_edge/layers/preprocessing/base_augmentation/#helia_edge.layers.preprocessing.base_augmentation.BaseAugmentation.augment_masks), [augment_sample()](/helia-edge/reference/api/helia_edge/layers/preprocessing/base_augmentation/#helia_edge.layers.preprocessing.base_augmentation.BaseAugmentation.augment_sample), [augment_targets()](/helia-edge/reference/api/helia_edge/layers/preprocessing/base_augmentation/#helia_edge.layers.preprocessing.base_augmentation.BaseAugmentation.augment_targets), [batch_augment()](/helia-edge/reference/api/helia_edge/layers/preprocessing/base_augmentation/#helia_edge.layers.preprocessing.base_augmentation.BaseAugmentation.batch_augment), [call()](/helia-edge/reference/api/helia_edge/layers/preprocessing/base_augmentation/#helia_edge.layers.preprocessing.base_augmentation.BaseAugmentation.call).

**Parameters**

| Name | Type | Default | Description |
| --- | --- | --- | --- |
| factor | float | 0.1 | Standard deviation of the Gaussian noise. |

Source: `helia_edge/layers/preprocessing/random_gaussian_noise.py:17`

### helia_edge.layers.preprocessing.random_gaussian_noise.RandomGaussianNoise1D.factor

`attribute` · `python`

```python
factor: tuple[float, float] = parse_factor(factor, min_value=0, max_value=None, param_name='factor')
```

Source: `helia_edge/layers/preprocessing/random_gaussian_noise.py:37`

### helia_edge.layers.preprocessing.random_gaussian_noise.RandomGaussianNoise1D.get_random_transformations

`method` · `python`

```python
get_random_transformations(input_shape: tuple[int, ...]) -> dict
```

Generate noise tensor

**Parameters**

| Name | Type | Default | Description |
| --- | --- | --- | --- |
| input_shape | tuple[int, ...] | Required | Input shape. |

**Returns**

| Name | Type | Description |
| --- | --- | --- |
| dict | dict | Dictionary containing the noise tensor. |

Source: `helia_edge/layers/preprocessing/random_gaussian_noise.py:39`

### helia_edge.layers.preprocessing.random_gaussian_noise.RandomGaussianNoise1D.augment_samples

`method` · `python`

```python
augment_samples(inputs) -> keras.KerasTensor
```

Apply sampled noise; inference bypasses sampling and application.

Source: `helia_edge/layers/preprocessing/random_gaussian_noise.py:61`

### helia_edge.layers.preprocessing.random_gaussian_noise.RandomGaussianNoise1D.get_config

`method` · `python`

```python
get_config()
```

Source: `helia_edge/layers/preprocessing/random_gaussian_noise.py:65`