FN-SSL

A python implementation of "FN-SSL: Full-Band and Narrow-Band Fusion for Sound Source Localization" , INTERSPEECH, 2023.

• Contributions

  • Full-Band and Narrow-Band Fusion for moving sound source localization, the full-band layers focus on learning the inter-frequency dependencies of spatial/localization cues. The narrow-band layers focus on exploiting these narrowband inter-channel information. Meanwhile, FNSSL has good generalization ability on real datasets.

• Extension to microphone array with the number of microphones larger than two:

  • for DP-IPD regression: You can refer to our SRP-DNN which is a 12-mic array.
  • for DOA classification: Modifying the dimensions of LSTM input data can extension to microphone array with more microphones.

Datasets

• Source signals: from LibriSpeech database
• Real-world multi-channel microphone signals: from LOCATA database

Quick start

• Preparation

  • Download the required dataset and organize the data according to the data_org in the data folder.

  • Generate multi-channel data, You can set data_num (in Simu.py) to control the size of the dataset. --train, -- test, --dev are used to control the generation of train dataset, test dataset, and validation dataset, respectively. The source data path of them are specified by dirs ['sousig_train '] in Opt.py.

  python Simu.py --train/--test/--dev
  

  • For DP-IPD regression, set is_doa = False (Model.FN_SSL), and use mse loss function, for DOA classification, set is_doa = True (Model.FN_SSL), and use ce loss function, meanwhile, the predgt2doa needs to be replaced synchronously. The initial Learning rate of doa classification is set to 5e-4.

  net = at_model.FN_SSL(is_doa=True/False)
  

• Training

  • For train step, --gpu-id is used to specify the gpu, ---bz corresponds to the batch size of train process, validation process, and test process, respectively.

  python Train.py --train --gpu-id [*] --bz * * * 
  

• Evaluation

  • In the inference stage, you can set checkpoints_dir (Predict. py) to select weights, we provide simulation dataset inference and locata dataset inference.
  • For simulated data evaluation

  python Predict.py --test --datasetMode simulate --bz * * *
  

  • For LOCATA dataset evaluation

  python Predict.py --test --datasetMode locata
  

• Pytorch Lightning version

  • We have re implemented FN-SSL using the Pytorch-lightning framework, which has a improvement in training speed compared to the torch.
  • For Train,

  python main.py fit --data.batch_size=[*,*] --trainer.devices=*,*
  

  • For test,

  python main.py test  --ckpt_path logs/MyModel/version_x/checkpoints/**.ckpt --trainer.devices=*,*
  

• Pretrained models

  • Using the FN_lightning model to load the lightning checkpoint in torch framework.

more checkpoints will be update soon.

Citation

If you find our work useful in your research, please consider citing:

@InProceedings{wang2023fnssl,
    author = "Yabo Wang and Bing Yang and Xiaofei Li",
    title = "FN-SSL: Full-Band and Narrow-Band Fusion for Sound Source Localization",
    booktitle = "Proceedings of INTERSPEECH",
    year = "2023",
    pages = ""}

Reference code

• SRP-DNN
• Cross3D

Licence

MIT