Environmental-Sound-Classification-ESC-using-neural-networks-and-other-classifiers

• Audio feature extraction and classification with the ECS-10 data set audio dataset
• ECS-10 audio data is included. It consists of 10 classes of different environmental sounds (sea waves, kids playing, etc.)
• The main goal is to compare classification accuracies for the 6 tested classifiers.

Dependencies

• Librosa (audio loading, audio visualization and feature extraction)
• Sci-kit learn
• Keras (Theano backend)
• Numpy, Matplotlib
• Pandas (data visualization)

Google Colab Notebook

A Google Colab Notebook (Python 3.7 Kernel) is added to illustrate the workflow.

The scripts for feature extraction and classification have been added as .ipynb files and are all loaded in the Jupyter Notebook sequentally.

Running feature_extraction.py creates a numpy array for features (feature.npy) and one for labels (label.npy). These files will be saved in the current directory.

Audio features extracted

• MFCC
• Chroma
• Mel spectrogram
• Tonal centroid feature
• Spectral contrast

Classifiers implemented

• Convolutional Neural Network (CNN)
• Multilayer Perceptron (MLP)
• Recurrent Neural Network (RNN)
• Support Vector Machine (SVM)
• Random Forest (RF)
• Naive Bayes (NB)
• KNearestNeighbors(KNN)

Accuracies obtained

Note: Direct comparison between classifiers can't be done yet since their parameters haven't been tuned to optimize accuracy yet. Out of 400 audio samples, the test set consisted on the 33% of this.

• CNN: 78.125% (100 epochs)
• MLP: 79.125 (100 epochs)
• RNN: 72% (100 epochs)
• SVM: 81.7%
• RF: 83%
• NB: 69.7%
• KNN: 67%

Approaches to improve accuracy

• Compute other features: MFCC + ZCR features improve classification accuracy for speech, noise and music labels. See if it also works for the 10 classes.
• Tune optimization hyperparameters (for every classifier): Weight initialization, decaying learning rate.
• Data scaling and feature normalization (MFCC)