https://github.com/GAMMA-UMD/doa-release

A Direction-of-Arrival estimation code repo accompanying our research paper.
https://github.com/GAMMA-UMD/doa-release

Last synced: 14 days ago
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A Direction-of-Arrival estimation code repo accompanying our research paper.

• Host: GitHub
• URL: https://github.com/GAMMA-UMD/doa-release
• Owner: GAMMA-UMD
• Created: 2019-09-14T21:34:35.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2020-02-09T19:31:34.000Z (almost 5 years ago)
• Last Synced: 2024-05-21T13:54:12.841Z (6 months ago)
• Language: Python
• Homepage:
• Size: 14.2 MB
• Stars: 65
• Watchers: 2
• Forks: 22
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        
# doa-release

Keras (w/ tensorflow backend) code accompanying our paper [Regression and Classification for Direction-of-Arrival Estimation with Convolutional Recurrent Neural Networks](https://arxiv.org/abs/1904.08452), Interspeech 2019

## Dataset

As described in the paper, our training/validation set is purely simulated data using a geometric sound propagation engine, whereas the test set only consists of real-world recorded data. To train/test our models, you need to download following files:

- Training/validation set features and labels [33.4GB] https://obj.umiacs.umd.edu/gammadata/dataset/doa/features.zip

- Test set original wav files and labels [19.2GB] https://obj.umiacs.umd.edu/gammadata/dataset/doa/SOFA_DOA_test_set.zip

- Training/validation set original wav files (you can skip this one if you don't want to extract the feature yourself) [43.8GB] https://obj.umiacs.umd.edu/gammadata/dataset/doa/wav.zip

The reason for providing test set in wav format instead of feature is that our models are trained assuming the ACN convention, whereas others may assume FuMa convention (see [Ambisonic formats](https://en.wikipedia.org/wiki/Ambisonic_data_exchange_formats)). In the test code, we provide a simple function for format conversion before feature extraction if needed.

## Models

In this repo, we attach several models under `models` folder. These are:

| Model name                           | Explanation                                                                                    | Convention |

|--------------------------------------|------------------------------------------------------------------------------------------------|------------|

| cartesian_base_model.h5              | Initial (untrained) regression model                                                           |     ACN    |

| cartesian_trained_model.h5           | Trained regression model                                                                       | ACN        |

| categorical_trained_model.h5         | Trained classification model                                                                   | ACN        |

| Perotin_categorical_trained_model.h5 | Baseline classification model by [Perotin et al.](https://hal.inria.fr/hal-01840453/document) | FuMa       |

Note that `categorical_trained_model.h5` is trained from `Perotin_categorical_trained_model.h5`, which is not different from training from scratch because their model uses FuMa convention that generates very different features from ACN convention.

## Usage

0. Download dataset and clone this repo.

1. Build conda environment and activate it:

    ```

    conda env create --file=environment.yml 

    conda activate doa-release

    ```

2. Training

    ```

    python3 train.py -i [train_feature_dir] -l [train_feature_dir]/train_labels.csv -o [output_dir] -lo [cartesian or categorical] -m models/[model_name]

    ```

3. Testing

    ```

    python3 test.py -i [test_wav_dir] -l [test_wav_dir]/test_labels.csv -m [trained_model_path] -lo [cartesian or categorical]

    ```

    When testing a model that uses FuMa convention, you must append `-c` to test.py's argument list, which enables conversion from ACN to FuMa. For example:

    ```

    python3 test.py -i [test_wav_dir] -l [test_wav_dir]/test_labels.csv -m models/Perotin_categorical_trained_model.h5 -lo categorical -c

    ```

## Citation

If you use our codes or models, please consider citing:

```

@inproceedings{Tang2019,

  author={Zhenyu Tang and John D. Kanu and Kevin Hogan and Dinesh Manocha},

  title={{Regression and Classification for Direction-of-Arrival Estimation with Convolutional Recurrent Neural Networks}},

  year=2019,

  booktitle={Proc. Interspeech 2019},

  pages={654--658},

  doi={10.21437/Interspeech.2019-1111},

  url={http://dx.doi.org/10.21437/Interspeech.2019-1111}

}

```

We also recommend citing the work of Perontin et al. if you also use their model:

```

@inproceedings{perotin2018crnn,

  title={CRNN-based joint azimuth and elevation localization with the Ambisonics intensity vector},

  author={Perotin, Laur{\'e}line and Serizel, Romain and Vincent, Emmanuel and Gu{\'e}rin, Alexandre},

  booktitle={2018 16th International Workshop on Acoustic Signal Enhancement (IWAENC)},

  pages={241--245},

  year={2018},

  organization={IEEE}

}

```