#Environmental Sound Classification using Deep Learning

A project from Digital Signal Processing course

#Dependencies

• Python 3.6
• numpy
• librosa
• pysoundfile
• sounddevice
• matplotlib
• scikit-learn
• tensorflow
• keras

#Dataset

Dataset could be downloaded at Dataverse or Github.

I'd recommend use ESC-10 for the sake of convenience.

Example:

├── 001 - Cat
│  ├── cat_1.ogg
│  ├── cat_2.ogg
│  ├── cat_3.ogg
│  ...
...
└── 002 - Dog
   ├── dog_barking_0.ogg
   ├── dog_barking_1.ogg
   ├── dog_barking_2.ogg
   ...

#Feature Extraction

Put audio files (.wav untested) under data directory and run the following command:

python feat_extract.py

Features and labels will be generated and saved in the directory.

#Classify with SVM

Make sure you have scikit-learn installed and feat.npy and label.npy under the same directory. Run svm.py and you could see the result.

#Classify with Multilayer Perceptron

Install tensorflow and keras at first. Run nn.py to train and test the network.

#Classify with Convolutional Neural Network

• Run cnn.py -t to train and test a CNN. Optionally set how many epochs to train on.
• Predict files by either:
  • Putting target files under predict/ directory and running cnn.py -p
  • Recording on the fly with cnn.py -P