Music Genre Classification Project

This repository is based on the recognition of musical genres through supervised and unsupervised learning.

Plugins:

• numpy: https://numpy.org
• librosa: https://librosa.org/doc/latest/index.html
• matplotlib: https://matplotlib.org
• pydub: https://pypi.org/project/pydub/
• pandas: https://pandas.pydata.org
• scikit-learn: https://scikit-learn.org/stable/

$ pip install -r requirements.txt

Information:

the dataset used for built this project is the notorious GTZAN dataset, recovered from kaggle (link to database: https://www.kaggle.com/datasets/andradaolteanu/gtzan-dataset-music-genre-classification).

In the utils directory, there are all that classes used for preprocessing the dataset and performing data augmentation (I did not use the csv file available at the previous link, I built my own).

• features_computation: computation of the various features to extract from audio files.
• features_extractions: extraction of the computed features to a csv file in a proper directory.
• features_visualizations: visualization of the single audio signals and the visualization of the various extracted features with a confrontation of the different genres.
• prepare_dataset: check the duration of audio files and perform data augmentation (30s long file -> ten 3s long chunk).

Then we have the core classes of the project:

• main: main class of the project that calls all the other.
• genres_ul_functions: performs k-means clustering and then performs its evaluation.
• genres_sl_functions: performs various classification algorithms (Neural Network, Random Forest, K-Nearest Neighbors, Support Vector Machine) and evaluate their performances with confusion matrix, roc curve and metrics (accuracy, F1-score,...).
• plot_functions: used for defining all the plot functions.
• constants: contains all the constants used in the project.

Performace Summary:

	MULTILAYER PERCEPTRON	RANDOM FOREST	K-NEAREST NEIGHBORS	SUPPORT VECTOR MACHINE
ACCURACY	84.80	79.33	89.80	89.40
F1-SCORE	0.85	0.79	0.90	0.89
EXECUTION TIME (sec)	63.72	52.76	7.73	21.40