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Urbansas Baseline

For a full description of the dataset visit our website.

Installation

conda create -n urbansas python=3.8
conda activate urbansas

# download the code
git clone https://github.com/magdalenafuentes/urbansas.git
cd urbansas

# install dependencies
pip install -e .

Downloading the dataset

The dataset is hosted on Zenodo. You will soon be able to download it using Soundata.

import soundata

dataset = soundata.initialize('urbansas')
dataset.download()  # download the dataset
dataset.validate()  # validate that all the expected files are there

If you downloaded the dataset using your own method (e.g. directly from Zenodo), either:

  • Move the dataset mv path/to/your/urbansas ~/sound_datasets/urbansas
  • Symlink the dataset ln -s path/to/your/urbansas ~/sound_datasets/urbansas
  • Change the path so it points to your downloaded dataset.
    1. Go to project_paths.py and set urbansas_root = "path/to/your/urbansas"

Hint: path/to/your/urbansas should contain the folders annotations/ audio/ video_2fps/

Setup

  1. Create the data index.

    This will compile a JSON file containing the files in the dataset, along with their ground truth labels as used in this project. You will need this for evaluation and training.

    python index_dataset.py --datasets Urbansas

    Confirm that the file Urbansas.json was created in the index/ folder.

    ls index/*.json

  2. Cache the dataset in the format used for training in this repo by running:

    python cache_dataset.py

Training the model

Follow these steps if your goal is to train the model from scratch and reproduce results seen in our ICASSP paper submission. This example trains a model with point sources (see paper). To train a boxwise model simply remove the flag --point_sources in all steps below and change the folder names accordingly.

Note: This code is organized for 5-fold cross validation. In the index/, each file object will contain a fold key, which tells us which fold that UID is found in. For example, when training on folds 1, 2, 3, and 4, we leave out fold 0 and only use it for evaluation.

Train each fold. Take note of the config_name that you use. This is the name that your model checkpoints will be saved under.

Note: if you want to reproduce the results only, skip the training and use the provided weights to make the predictions!

python train_model.py \
    --train_dataset Urbansas --val_dataset Urbansas \
    --train_folds 1 2 3 4 --val_folds 0 \
    --config_name urbansas_f0_point_sources \
    --point_sources --filter_confirmed

For information on batch job generation over parameter grid or training in an environment using Slurm and or Singularity, see the documentation in jobs.py.

Get some predictions!

To predict on just one left-out fold, run

python predict_model.py \
    --config_name urbansas_f0_point_sources \
    --folds 0 --point_sources --filter_confirmed

Your predictions will be saved in predictions/<your_config_name>. Inside here you'll find a .h5 file containing the predictions.

Evaluate

Load these up and evaluate them agains the ground truth:

python evaluate_model.py \
    --config_name urbansas_f0_point_sources \
    --point_sources

See the results

To compute the mean results as the paper, run the following notebook.

jupyter lab notebooks/results.ipynb

Visually inspect the results

You can also visually inspect some of this results using the notebook below.

jupyter lab notebooks/viz.ipynb

Citation

If you are using this code or our dataset please cite the following paper:

"Urban sound & sight: Dataset and benchmark for audio-visual urban scene understanding."
Fuentes, M., Steers, B., Zinemanas, P., Rocamora, M., Bondi, L., Wilkins, J., Shi, Q., Hou, Y., Das, S., Serra, X. and Bello, J.P., 
in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022.

@inproceedings{urbansas_2022,
  title={Urban sound \& sight: Dataset and benchmark for audio-visual urban scene understanding},
  author={Fuentes, Magdalena and Steers, Bea and Zinemanas, Pablo and Rocamora, Mart{\'\i}n and Bondi, Luca and Wilkins, Julia and Shi, Qianyi and Hou, Yao and Das, Samarjit and Serra, Xavier and others},
  booktitle={ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={141--145},
  year={2022},
  organization={IEEE}
}

License

The ICASSP 2022 Urbansas Baseline is open-sourced under the BSD-3-Clause license. See the LICENSE file for details.

For a list of other open source components included in ICASSP 2022 Urbansas Baseline, see the file 3rd-party-licenses.txt.