Conda dependencies.

README.md

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+# 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](#), 
+[Y. Li](#), 
+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:https://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: __url to be announced__
+
+**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!
+
+----
+
+## 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, you will need 
+
+### Data set-up
+
+----
+
+## Conducting the experiments

conda_dependencies.yaml

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+name: dnd-sed
+channels:
+ - pytorch
+ - anaconda
+ - conda-forge
+ - defaults
+dependencies:
+ - loguru=0.3.2=py37_1
+ - numpy=1.17.4=py37hc1035e2_0
+ - pytorch=1.3.1=py3.7_cuda10.1.243_cudnn7.6.3_0
+ - pyyaml=5.1.2=py37h7b6447c_0
+