Urban Sounds Classification in MXNet Gluon

This example provides an end-to-end pipeline for a common datahack competition - Urban Sounds Classification Example.

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 following 10 labels:

siren,
street_music,
drilling,
dog_bark,
children_playing,
gun_shot,
engine_idling,
air_conditioner,
jackhammer,
car_horn

To be able to run this example:

1. pip install -r requirements.txt

   If you are in the directory where the requirements.txt file lies, 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

   Assuming you are in a directory “UrbanSounds”, after downloading and extracting train.zip, the folder structure should be:

        UrbanSounds        
                    - Train
                        - 0.wav, 1.wav ...
                    - train.csv
                    - train.py
                    - predict.py ...
   

4. Apache MXNet is installed on the machine. For instructions, go to the link: https://mxnet.apache.org/install/

For information on the current design of how the AudioFolderDataset is implemented, refer below: https://cwiki.apache.org/confluence/display/MXNET/Gluon+-+Audio

Usage

For training:

• Arguments
  • train : The folder/directory that contains the audio(wav) files locally. Default = “./Train”
  • csv: The file name of the csv file that contains audio file name to label mapping. Default = “train.csv”
  • epochs : Number of epochs to train the model. Default = 30
  • batch_size : The batch size for training. Default = 32

To use the default arguments, use:

python train.py

or

To pass command-line arguments for training data directory, epochs, batch_size, csv file name, use :

python train.py --train ./Train --csv train.csv --batch_size 32 --epochs 30 

For prediction:

• Arguments
  • pred : The folder/directory that contains the audio(wav) files which are to be classified. Default = “./Test”

To use the default arguments, use:

python predict.py

or

To pass command-line arguments for test data directory, use :

python predict.py --pred ./Test