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└── checkpoint_tag
├── checkpoint_name.pth # PyTorch checkpoint with model state dict under 'state_dict' key.
└── config.yaml # Hydra config used to train the model{latent_size}_{training_dataset}_{loss_functions_used}_{reconstruction_algorithm}latent_size is either 16x16 or 32x32, depends on the neural audio codec used in the dataset.random or librispeech. For librispeech, a groupped version can be used, tagged as
group_n_m_r_c (see LenslessMic Version of Librispeech
(with 288x288 after group if the sensor image size is not the default 256x256). The version of the model, which is
fine-tuned using train-other, is tagged as librispeech_other and _ft at the end.loss_function is usually MSE, SSIM, and Raw SSIM, as in the paper. We also provide checkpoints with only MSE,
MSE and SSIM, and all three with L1 waveform or Mel Losses.PSF_Unet4M_U5_Unet4M is the Learned and R-Learned methods from the paper.
Unet8M is the NoPSF method.1@article{grinberg2025lenslessmic,
2 title = {LenslessMic: Audio Encryption and Authentication via Lensless Computational Imaging},
3 author = {Grinberg, Petr and Bezzam, Eric and Prandoni, Paolo and Vetterli, Martin},
4 journal = {arXiv preprint arXiv:2509.16418},
5 year = {2025},
6}