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Music Genre Classification Project\n\nThis repository is based on the recognition of musical genres through supervised and unsupervised learning.\n\n\u003cimg \n  width=\"1843\" \n  alt=\"apr_project_architecture\" \n  src=\"https://user-images.githubusercontent.com/32509505/236864331-c288575f-e6cc-4bfb-b419-0d60ba82cb0e.png\"\n  title=\"High level architecture of the project\"\u003e\n\n## Plugins:\n- numpy: https://numpy.org\n- librosa: https://librosa.org/doc/latest/index.html\n- matplotlib: https://matplotlib.org\n- pydub: https://pypi.org/project/pydub/\n- pandas: https://pandas.pydata.org\n- scikit-learn: https://scikit-learn.org/stable/\n```\n$ pip install -r requirements.txt\n```\n\n## Information:\nthe dataset used for this project is the notorious GTZAN dataset, recovered from kaggle (_**link to database:** https://www.kaggle.com/datasets/andradaolteanu/gtzan-dataset-music-genre-classification_). \n\nIn the **utils** directory,\nthere are all that classes used for preprocessing the dataset and performing data augmentation\n(I did not use the csv file available at the previous link, I built my own).\n\n- **_features_computation:_** computation of the various features to extract from audio files.\n- _**features_extractions:**_ extraction of the computed features to a csv file in a proper directory.\n- _**features_visualizations:**_ visualization of the single audio signals and the visualization of the various extracted features with a confrontation of the different genres.\n- _**prepare_dataset:**_ check the duration of audio files and perform data augmentation (30s long file -\u003e ten 3s long chunk).\n\nThen we have the core classes of the project:\n\n- _**main:**_ main class of the project that calls all the other. \n- _**genres_ul_functions:**_ performs k-means clustering and then performs its evaluation. \n- **_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,...).\n- **_plot_functions:_** used for defining all the plot functions.\n- **_constants:_** contains all the constants used in the project.\n\n## Performace Summary:\n\n|   | MULTILAYER PERCEPTRON | RANDOM FOREST | K-NEAREST NEIGHBORS | SUPPORT VECTOR MACHINE |\n| - | --------------------- | ------------- | ------------------- | ---------------------- |\n| ACCURACY  | 84.80 | 79.33 | 89.80 | 89.40 |\n| F1-SCORE  | 0.85 | 0.79 | 0.90 | 0.89 |\n| EXECUTION TIME (sec) | 63.72 | 52.76 | 7.73 | 21.40 |\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsabaudian%2Fmusic_genre_classification_project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsabaudian%2Fmusic_genre_classification_project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsabaudian%2Fmusic_genre_classification_project/lists"}