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Music Genre Recommender website that can identify and recommend 10 different genres of music using Light Gradient Boosting Machine (LGBM). An accuracy of 90% was achieved on the test set by tuning the hyperparameters of the model with Optuna.

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Music Genre Recommender

Music Genre Recommendation website that can identify and recommend 10 different genres of music using Light Gradient Boosting Machine (LGBM). The model achieves an accuracy of 90% on the test set and an F1 score of 0.90. The training data consists of 1000 audio samples each of a duration of 30 seconds. The model is deployed using Flask.

Optuna was used to perform hyperparameter tuning and improve the accuracy of the model by 8% (from 82% to 90%). Once an audio file is uploaded to the website by the user, 58 different features are extracted and passed to the model to accurately identify the genre of music. Relevant song recommendations are generated using cosine similarity and the classified genre of music.

Features

  • Upload .wav files for music recognition and recommendation
  • Validate the type of file uploaded to the website
  • Predict the genre of music
  • Get top 3 song recommendations
  • Play the recommended songs on the website
  • Display a loading icon while predicting and recommending songs to the user

Dataset Used

https://www.kaggle.com/datasets/andradaolteanu/gtzan-dataset-music-genre-classification

Training and Recommendations

Flask App

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Comparison of Different ML Models

Machine Learning Model Test Accuracy F1 Score
Light Gradient Boosting Machine (Optimized) 90% 0.902
Cat Boost Classifier (Default) 85% 0.852
XGBoost Classifier (Optimized) 85% 0.849
Random Forest Classifier (Optimized) 84% 0.841
Random Forest Classifier (Default) 82% 0.827
Gradient Boosting Classifier (Default) 82% 0.823
Light Gradient Boosting Machine (Default) 82% 0.818
XGBoost Classifier (Default) 81% 0.808
Support Vector Classifier (Default) 76% 0.753
Logistic Regression (Default) 73% 0.729
KNN (Default) 69% 0.695
Decision Tree Classifier (Default) 59% 0.582

Usage

Clone the repository

git clone https://github.com/rprkh/Music-Genre-Recognizer.git

Navigate to the root directory of the project

cd Music-Genre-Recognizer

Install the requirements

pip install -r requirements.txt

Navigate to the Flask App folder

cd "Flask App"

Run the app.py script

python app.py

The website should start on http://127.0.0.1:2000/

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Music Genre Recommender website that can identify and recommend 10 different genres of music using Light Gradient Boosting Machine (LGBM). An accuracy of 90% was achieved on the test set by tuning the hyperparameters of the model with Optuna.

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