This repository contains the implementation for:

• Sina Khanagha, Bunlong Lay, Timo Gerkmann, "Bone-conduction Guided Multimodal Speech Enhancement with Conditional Diffusion Models", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Spain, May 2026.

The code base is mostly adopted from our group's previous work SGMSE+

You can clone the repository and install the required dependencies with:

git clone https://github.com/sp-uhh/bcdm.git
cd bcdm
pip install -r requirements.txt

You can use the train.py script for training the model. For example, to train the BCDM-IC-L model from our paper you can use the following command:

python train.py --base_dir <path_to_your_dir> --format conditional_bc --backbone ncsnpp_v2 

And for BCDM-DC-L:

python train.py --base_dir <path_to_your_dir> --format conditional_bc --backbone ncsnpp_v2_decoder_injection 

Note that the some of the available options from python train.py --help are not currently implemented here.

• For resuming training, you can use the --ckpt option of train.py
• If you do not have a wandb account setup, you can also pass --nolog for offline logging
• where your_base_dir should be a path to a folder containing subdirectories train/ and valid/ (optionally test/ as well). Each subdirectory must itself have three subdirectories clean/, noisy/ and acc/ (containing bone-conducted sensor data), with the same filenames present in all three subdirectories. Alternatively you can modify the sgmse/data_module.py file to match your dataset structure.

To evaluate on a test set, run

python enhancement.py --test_dir <your_test_dir> --conditional_dir <your_bone-conducted_dir> --enhanced_dir <enhanced_files_output_dir> --ckpt <path_to_model_checkpoint> --N <num_reverse_steps>

to generate the enhanced .wav files, and subsequently run

python calc_metrics.py --test_dir <your_test_dir> --enhanced_dir <your_enhanced_dir>

to calculate and output the instrumental metrics.

We kindly ask you to cite our papers in your publication when using any of our research or code: TODO