Implementation of PSM: Learning Probabilistic Embeddings for Multi-scale Zero-Shot Soundscape Mapping
in ACM Multimedia 2024

Request for data used in our work, available to be read by webdataset for both GeoSound and SoundingEarth dataset. Each sample contains CLAP-processed mel-spectrogram features for audio, satellite image and associated metadata.

To download raw audios from the audio sources: aporee, freesound, and iNaturalist, create account (and request for API key if necessary), then find respective metadata .csv files located here, and utilise the following data-download scripts for each of these sources:

aporee: ./geoclap/data_prep/get_SoundingEarth_raw_audio.sh

iNaturalist: ./geoclap/data_prep/iNaturalist_download.py

freesound: ./geoclap/data_prep/freesound_download.py

yfcc: For YFCC, first yfcc-videos need to be downloaded and then audio should be extracted from those videos. Refer to yahoo100m section of ./geoclap/data_prep/README.md for details on this.

1. Clone this repo

   git clone git@github.com:mvrl/PSM.git
   cd PSM/geoclap
   

2. Setting up enviornment

   conda env create --file environment.yml
   conda activate sat2audio
   

   Note: Instead of conda it could be easier to pull docker image ksubash/sat2audio:2.0 for the project we provide using following steps:

   docker pull ksubash/sat2audio:2.0
   docker run -v $HOME:$HOME --gpus all --shm-size=64gb -it ksubash/geoclap
   source /opt/conda/bin/activate /opt/conda/envs/sat2audio_demo
   

3. Copy the pre-trained checkpoint of SATMAE named as pretrain-vit-base-e199.pth provided in this google drive folder to the location pointed by cfg.satmae_pretrained_ckpt.

4. Check config.py and setup paths by manually creating relevant directories if needed.

5. Assuming that the data is downloaded and paths in config.py are properly setup, we are now ready to run experiments related to PSM. Change directory such that we can run geoclap as a python module.

   cd ../
   

6. Assuming wandb is set up correctly for logging purpose, we can now launch the PSM training as follows:

   python -m geoclap.train --num_workers 8 \
                           --probabilistic true \
                           --metadata_type latlong_month_time_asource_tsource \
                           --run_name GeoSound_pcmepp_metadata_sentinel \
                           --dataset_type GeoSound \
                           --sat_type sentinel \
                           --mode train \
                           --wandb_mode online
   
   

7. Once the training is complete and we have the appropriate checkpoint of the model, we can evaluate the cross-modal retrevial performance of the model. For example,

   python -m geoclap.evaluate --ckpt_path GeoSound_pcmepp_metadata_sentinel_best_ckpt_path \
                              --loss_type pcmepp \
                              --dataset_type GeoSound \
                              --test_zoom_level 0 \
                              --sat_type sentinel \
                              --metadata_type latlong_month_time_asource_tsource \
                              --add_text true \
                              --meta_droprate 0 \
                              --test_mel_index 0 
   

The best checkpoints for our experiments in the paper can be found here. Please note that these checkpoints are saved under directory with wandb-generated random name for each experiments, therefore refer to the file: ./geoclap/ckpt_paths.py to find appropriate checkpoint path.

@inproceedings{khanal2024psm,
  title = {PSM: Learning Probabilistic Embeddings for Multi-scale Zero-Shot Soundscape Mapping},
  author = {Khanal, Subash and Eric, Xing and Sastry, Srikumar and Dhakal, Aayush and Xiong Zhexiao and Ahmad, Adeel and Jacobs, Nathan},
  year = {2024},
  month = nov,
  booktitle = {ACM Multimedia},
}

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