kalyc commented on a change in pull request #13325: [MXNET-1210 ] Gluon Audio - 
Example
URL: https://github.com/apache/incubator-mxnet/pull/13325#discussion_r237254592
 
 

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 File path: example/gluon/urban_sounds/README.md
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+# Urban Sounds classification in MXNet
+
+This example provides an end-to-end pipeline for a common datahack competition 
- Urban Sounds Classification Example.
+Below is the link to the competition:
+https://datahack.analyticsvidhya.com/contest/practice-problem-urban-sound-classification/
+
+After logging in, the data set can be downloaded.
+The details of the dataset and the link to download it are given below:
+
+
+Urban Sounds Dataset:
+## Description
+  The dataset contains 8732 wav files which are audio samples(<= 4s)) of 
street sounds like engine_idling, car_horn, children_playing, dog_barking and 
so on.
+  The task is to classify these audio samples into one of the 10 labels.
+
+To be able to run this example:
+
+1. `pip install -r ./requirements.txt`
+
+    This step installs the required libraries to run the example.
+    The main dependency that is required is: Librosa. 
+    The version used to test the example is: `0.6.2`
+    For more details, refer here:
+*https://librosa.github.io/librosa/install.html*
+
+2. Download the dataset(train.zip, test.zip) required for this example from 
the location:
+https://drive.google.com/drive/folders/0By0bAi7hOBAFUHVXd1JCN3MwTEU
+
+3. Extract both the zip archives into the **current directory** - after 
unzipping you would get 2 new folders namely,\
+   **Train** and **Test** and two csv files - **train.csv**, **test.csv**
+
 
 Review comment:
   please add comment about how the folder structure should look like for more 
clarity

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