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1st place solution to the DCASE 2019 - Task 5 - Urban Sound Tagging
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DCASE 2019 - Task 5 - Urban Sound Tagging

This repository contains the final solution that I used for the DCASE 2019 - Task 5 - Urban Sound Tagging. The model achieved 1st position in prediction of both Coarse and Fine-level labels.

Reproducing the results


  • Linux based system
  • Python >= 3.5
  • NVidia GFX card with at least 8GB memory
  • Cuda >= 10.0
  • virtualenv package installed


Clone this repository. For a single command to replicate the entire solution, execute make run_all command while being in the repository directory. This command does the following steps sequentially:

  • make env: Creates a virtual environment in the current directory
  • make reqs: Installs python packages
  • make pytorch: Installs PyTorch
  • make download: Downloads the Task 5's data from Zenodo
  • make extract: Extracts the zipped files
  • make parse: Parses annotations
  • make logmel: Computes and saves Log-Mel spectrograms for all the files
  • make train_s1: Trains (system 1) model
  • make eval_s1: Conducts local evaluation of the trained model (system 1)
  • make submit_s1: Generates the submission file (system 1)
  • make train_s2: Trains (system 2) model
  • make eval_s2: Conducts local evaluation of the trained model (system 2)
  • make submit_s2: Generates the submission file (system 2)


The weights for both the models are available in the releases page.

About the solution

The technical report can read here


Unless otherwise stated, the contents of this repository are shared under the MIT License.

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