This repository is open for contributions from everyone at college, as a part of the annual Kalanjali events. Make your commits count!
A basic Machine Learning model with Jupyter framework which analyzes the given sample and recognizes its genre.
A music genre is basically categorization of pieces of music.It is to be distinguished from musical form and musical style.
Music can be divided into genres in varying ways, such as into pop music, jazz music, metal music, hip hop music, blues music and so on. The artistic nature of music means that these classifications are often subjective. So few genres may overlap.
This is an open-source project, in which we have gathered data and predict the genre of a given music sample. We are predicting the genre of a song based on certain parameters in prominent datasets found online.This simple ML Model can be developed further and can be applied into different music based softwares.
By further developing this model, the music based softwares can analyze the user preferences and create a user friendly interface.
Colab is a free Jupyter notebook environment that runs entirely in the cloud. Most importantly, it does not require a setup and the notebooks that you create can be simultaneously edited by your team members - just the way you edit documents in Google Docs. Colab supports many popular machine learning libraries which can be easily loaded in your notebook.
⚙Underlying tools:
Visual Studio code (or) Google Colab (or) Jupyter notebook.
📃Algorithms:
KNN (K-Nearest Neighbor) algorithm
The K-Nearest Neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems.
- Download the source dataset and test set from the links given below.
- Open the .ipynb file in Google colab (or) Visual studio code
- Install necessary libraries if they aren't present already.
- Mention the dataset and testset file adresses in the required places.
1)Source dataset: https://www.kaggle.com/andradaolteanu/gtzan-dataset-music-genre-classification
2)Test dataset: https://uweb.engr.arizona.edu/~429rns/audiofiles/audiofiles.html
https://www.researchgate.net/publication/337001430_Machine_Learning_for_Music_Genre_Classification
https://www.javatpoint.com/k-nearest-neighbor-algorithm-for-machine-learning
https://www.youtube.com/watch?v=Pqo9o0286Qs
Follow the steps below to contribute to this repository:
- Fork this repository onto your account.
- Commit all your changes into the forked repository.
- Create a pull request and we'll review your commits!
For a complete guide on open source and contributions, watch this video.