Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/dr-costas/dnd-sed

Sound event detection with depthwise separable and dilated convolutions.
https://github.com/dr-costas/dnd-sed

audio-signal-processing deep-learning deep-neural-networks depthwise-separable-convolutions depthwiseseparableconvolution dilated-cnn dilated-convolution machine-learning machine-listening sound-event-detection

Last synced: 2 months ago
JSON representation

Sound event detection with depthwise separable and dilated convolutions.

Awesome Lists containing this project

README 

        
# Sound event detection with depthwise separable and dilated convolutions



----



### 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](https://tutcris.tut.fi/portal/en/persons/konstantinos-drosos(b1070370-5156-4280-b354-6291618bb965).html), 

[S. I. Mimilakis](https://www.idmt.fraunhofer.de/en/institute/doctorands/mimilakis.html), 

[S. Gharib](https://scholar.google.com/citations?user=neb2vi0AAAAJ&hl=en), 

[Y. Li](https://scholar.google.com/citations?user=ywDuJjEAAAAJ&hl=en), 

and [T. Virtanen](https://tutcris.tut.fi/portal/en/persons/tuomas-virtanen(210e58bb-c224-40a9-bf6c-5b786297e841).html).



Our code is based on [PyTorch framework](https://pytorch.org/) 

and we use the publicly available dataset 

[TUT-SED Synthetic 2016](http://www.cs.tut.fi/sgn/arg/taslp2017-crnn-sed/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)](https://wcci2020.org/).  



You can find an online version of [our paper at arXiv](https://arxiv.org/abs/2002.00476).



**If you use our method, please cite our paper.**  



----



## Table of Contents

1. [Method introduction](#method-introduction)

2. [System set-up](#system-set-up)

3. [Conducting the experiments](#conducting-the-experiments)



----



## 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](https://arxiv.org/abs/2002.00476)!



----



## 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: 



````shell script

$ 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:



````shell script

$ 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](http://www.cs.tut.fi/sgn/arg/taslp2017-crnn-sed/tut-sed-synthetic-2016).

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. 



----



## 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!