Project Title:Music Genre Classification Using Machine Learning

Objective:

To build a machine learning model that can automatically classify music tracks into different genres based on audio features.

Summary:

This project aims to classify music into genres like rock, pop, jazz, classical, or hip-hop using machine learning. The system learns from audio features—such as tempo, pitch, rhythm, and timbre—extracted from music files. These features are either directly used in a traditional classifier (like SVM or Random Forest) or converted into spectrogram images for deep learning models like CNNs.

The model is trained on a labeled dataset, such as the GTZAN dataset, which contains music samples labeled by genre. After training, the system can predict the genre of new, unseen tracks with reasonable accuracy.

Key Steps:

Collect Dataset – Use a labeled dataset like GTZAN or Free Music Archive (FMA).

Extract Features – Use libraries like Librosa to extract MFCCs, chroma, and spectrograms.

Train Model – Use machine learning (SVM, KNN) or deep learning (CNN, RNN) models.

Evaluate – Test accuracy and confusion matrix for genre predictions.

Technologies Used:

Python

Librosa (audio processing)

Scikit-learn / TensorFlow / PyTorch

Matplotlib / Seaborn (for visualization)

Applications:

Music streaming services (e.g., Spotify, YouTube Music)

Music recommendation systems

Audio content tagging

DJ tools and music organization apps

Expected Outcomes:

A trained model that predicts the genre of a music track

Accuracy metrics and confusion matrix

Optionally, a simple GUI or web app for uploading and classifying music