---
license: mit
task_categories:
- audio-to-audio
language:
- en
---

# Dataset Card for LenslessMic Version of N(0,1) Random Dataset

## Dataset Summary
A LenslessMic version of the N(0,1) random images dataset from the
["LenslessMic: Audio Encryption and Authentication via Lensless Computational Imaging"](https://arxiv.org/abs/2509.16418) paper.
The dataset can be used to train a codec-agnostic reconstruction algorithm.

| Partition   | # Audio | # Frames |
|--------------|---------|----------|
| train        | 200     | 30000    |

**Note**: We split dataset into 200 files, however, there are no actual audio files. Only frames are used.

To download the dataset and work with it, use our [official repository](https://github.com/Blinorot/LenslessMic).

Dataset is collected using [DigiCam](https://arxiv.org/abs/2502.01102). Setup configuration:

| Parameter                                 | Value              |
|-------------------------------------------|--------------------|
| Screen Size                               | [1920, 1200]       |
| Screen Pixel-Pitch                        | 0.27 mm            |
| Screen-To-Mask Distance                   | 30e-2 m            |
| Sensor Size                               | [4056, 3040]       |
| Sensor Size Downsample Coefficient        | 8                  |
| Sensor Pixel-Pitch                        | 1.55 × 10⁻⁶ m      |
| Mask-To-Sensor Distance                   | ≈ 4e-3 m           |
| Image size on the Screen (256 case)       | 928 × 928          |
| Image size on the Screen (288 case)       | 1044 × 1044        |
| Vertical Shift on the Screen (256 case)   | -23                |
| Vertical Shift on the Screen (288 case)   | -20                |
| Number of masks                           | 100                |
| Mask Aperture Shape (for 1/3 channels)    | [18, 24]           |
| Mask Center                               | [55, 77]           |

For other configuration, please refer to the codebase above.

## Dataset Structure
Dataset is structured in the following format:

```
.
└── partition_name
    └── image_size # 16x16 or 32x32
        ├── lensed # lensed version of the video representation
        |   └── filename_i.mkv # normalized video representation of i-th audio file using this codec
        └── lensless_measurement # lensless version captured using LenslessMic
            ├── filename_i.mkv # lensless video of the i-th audio file
            ├── filename_i.txt # label 'j' of the mask from the masks dir used for this video
            └── masks # masks for the lensless camera
                └── mask_j.npy # mask pattern
```

Apart from other LenslessMic datasets, this one does not use any audio codecs. These are just random images from N(0,1).
The dataset can be used to train a codec-agnostic reconstruction algorithm. No min/max vals are used (set to 0 and 1).

Some codecs have different types of lensless measurements:

1. `lensless_measurement`: standard version. Resizes images in a screen in a such a way that they have size 256x256 on the sensor.

Region of interest for the reconstruction for this dataset is:

| Sensor Image Size | Top Left Corner | Height | Width |
| ----------------- | --------------- | ------ | ----- |
| 256 x 256         | [65, 118]       | 256    | 256   |


## Citation

If you use this dataset, please cite it as follows:

```bibtex
@article{grinberg2025lenslessmic,
  title = {LenslessMic: Audio Encryption and Authentication via Lensless Computational Imaging},
  author = {Grinberg, Petr and Bezzam, Eric and Prandoni, Paolo and Vetterli, Martin},
  journal = {arXiv preprint arXiv:2509.16418},
  year = {2025},
}
```