splumecocq / acousticscenesclassification

Python project of acoustic scenes classification

Acoustic Scenes Classification

This project shares the python code used for the challenge DCASE 2018 task 1.

The goal of Acoustic Scene Classification is to classify a test recording into one of the provided predefined classes that characterizes the environment in which it was recorded

In this project the audio signal in input is preprocessed into a spectrogram by short-time Fourier transform SFTF. A methodology of supervised machine learning based on Convolutional Neuronal Network (CNN) classified the signal.

Struture

The program main.py launches the training.

The directory Models contains the CNN modules:

• simpleCNN: simple module to test the process
• simpleCNN_v2: same with a standard conv: conv2D + batchNorm + Relu
• CNN_Dorfer: CNN proposed by CP-JKU team to the DCASE Challenge 2018 Task 1 Subtask A (without the Gaussian Noise)
• CNN_Dorfer2: CNN proposed by CP-JKU team to the DCASE Challenge 2018 Task 1 Subtask A (with the Gaussian Noise)
• Xception: Xception modele with variable number of Middle Flow and simplified Exit Flow (one conv2d and average pooling)

The directory Data contains:

• the split of train and validation/test as in the challenge DCASE 2018 (fold2_train.txt and fold2_evaluate.txt)
• the split of initial train in train and validation (fold3_train.txt and fold3_val.txt)
• mean and standard deviation on train data (mean_train.npy and std_train.npy)

The directory Utils contains:

• audio_to_spectrogram: Convert audio signal to spectrogram with Librosa librairy
• progressbar.py: a simple progress to follow the evolution of training process
• save_model: Save a model in a file
• unzip: extract the zip file of input data