Music Genre Classification on GTZAN. Submitted for the final project

This repository contains a bunch of small tools which I wrote as a part of my Master's Thesis.

Evaluación de sistemas de estimación del género musical mediante descriptores de audio y aprendizaje automático

Music Genre Classification using Logistic Regression

Classification of music genres using audio files.

Music genre classification done in two different ways: 1.Traditional ML and 2.Temporal feature integration/Fusion of decisions

The Mini Project for Semester 5: Vidyalankar Institute of Technology

Implemented multiple classification models using Python with GTZAN data set by leading a team of three people. Compared the performances of K-NN, SVM, CNN models and logged their results in terms of prediction accuracies. A Convolutional Neural Network model stood out with the highest prediction accuracy of 82% amongst all other models.

A music genre classifer system

A music genre classifier using CNN, RNN, and Mel spectrogram

10 genre music genre classification. Comparative and exploratory analysis of the pre-processing routines applied, feature extraction techniques and the models used for identification are enclosed

A ML approach to find genre of a given song file and to recommend similar songs in the same dataset.

This is a small project to classify the GTZAN dataset by applying multiple algorithms for training the models.

Music Genre Classification using Neural Networks on GTZAN Dataset

Using the GTZAN dataset to create a classifier that can classify what the music genre is playing!

GTZAN - Music Genre Classification

Music genre classification project as part of the Numerical Analysis for Machine Learning course at Politecnico di Milano, A.Y 2022-2023.