Sound event detection with depthwise separable and dilated convolutions

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Welcome to the repository of DnD-SED method.

This is the repository for the method presented in the paper "Sound Event Detection with Depthwise Separable and Dilated Convolutions", by K. Drossos, S. I. Mimilakis, S. Gharib, Y. Li, and T. Virtanen.

Our code is based on PyTorch framework and we use the publicly available dataset TUT-SED Synthetic 2016.

Our paper is submitted for review to the IEEE World Congress on Computational Intelligence/International Joint Conference on Neural Networks (WCCI/IJCNN).

You can find an online version of our paper at arXiv.

If you use our method, please cite our paper.

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Table of Contents

1. Method introduction
2. System set-up
3. Conducting the experiments

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Method introduction

Methods for sound event detection (SED) are usually based on a composition of three functions; a feature extractor, an identifier of long temporal context, and a classifier. State-of-the-art SED methods use typical 2D convolutional neural networks (CNNs) as the feature extractor and an RNN for identifying long temporal context (a simple affine transform with a non-linearity is utilized as a classifier). This set-up can yield a considerable amount of parameters, amounting up to couple of millions (e.g. 4M) Additionally, the utilization of an RNN impedes the training process and the parallelization of the method.

With our DnD-SED method we propose the replacement of the typical 2D CNNs used as a feature extractor with depthwise separable convolutions, and the replacement of the RNN with dilated convolutions. We compare our method with the widely-used CRNN method, using the publicly available TUT-SED Synthetic 2016 dataset. We conduct a series of 10 experiments and we report mean values of time needed for one training epoch, F1 score, error rate, and amount of parameters.

We achieve a considerable decrease at the computational complexity and a simultaneous increase on the SED performance. Specifically, we achieve a reduction of the amount of parameters and the mean time needed for one training epoch (reduction of 85% and 72% respectively). Also, we achieve an increase of the mean F1 score by 4/6% and a reduction of the mean error rate by 3.8%.

You can find more information in our paper!

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System set-up

To run and use our method (or simply repeat the experiments), you need to set-up the code and use the specific dataset. We provide you the full code used for the method, but you will have to get the audio files and extract the features.

Code set-up

To set-up the code and run our code, you will need to clone this repository and then install the dependencies using your favorite package manager. If you are using Conda, then you can do:

$ conda env create --yes --file conda_dependencies.yml

Then, an environment with the name dnd-sed will be created, using Python 3.7. If you prefer PIP, then you can do:

$ pip install -r pip_dependencies.txt

And you will be good to go! If anything is not working, please let me know by making an issue in this repository.

Data set-up

To set-up the data, you first have to follow the procedure and download the data from the corresponding web-page. Then, you should create your input/output values and use them with our method.

The code in this repository offers data handling functionality. The data_feders.get_tut_sed_data_loader function returns a PyTorch data loader, using as a dataset class the data_feders.TUTSEDSynthetic2016.

To use your extracted features with the class, you should have saved the features and the target values as separate files. You can specify the file names and the directory having these files in the settings files.

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Conducting the experiments

In the settings directory you can find all the settings that were used for the results presented in the paper. We uses each settings file 10 times, and then we averaged the results. If you want to reproduce our results, then please remember to follow our procedure.

To run the code you just have to use the main.py script, passing the proper arguments. The needed arguments for running the main.py script are:

• The name of the model that will be used, -m. Accepted values are:
  1. baseline -- This is the baseline, CRNN model.
  2. baseline_dilated -- This is the baseline model, but with the RNN replaced by a CNN with dilated convolution.
  3. dessed -- This is the baseline model, but with the CNNs replaced by depth-wise separable convolutions.
  4. dessed_dilated -- This is our proposed model, with depth-wise separable convolutions, followed by dilated convolution.
• The name of the settings file to be used (without the extension .yaml): -c. For example, if the settings file synthetic_2016_k_55_d_1_1.yaml is to be used, then this argument has be synthetic_2016_k_55_d_1_1.

There are some optional arguments for the main.py script. These are:

• The extension of the settings file, -e. Default value is .yaml.
• The directory where the settings file is ,-d. Default value is settings.

Enjoy!