Music Genre Classification Using Mel Spectrograms
Published on Dec 2023
A machine learning project focused on classifying music genres by analyzing their audio features, specifically using Mel Spectrograms as input.
Project Overview
This project explores the application of machine learning, particularly deep learning, to the task of automatic music genre classification. The core idea is to transform raw audio signals into Mel Spectrograms, which are powerful visual representations of sound, and then train a convolutional neural network (CNN) to identify distinct musical genres.
Technical Details:
- Data Preprocessing: Converting audio files into Mel Spectrograms, which represent the spectrum of sounds as heard by the human ear.
- Feature Extraction: Mel Spectrograms themselves act as rich features for the model.
- Model Architecture: Utilizing Convolutional Neural Networks (CNNs), which are highly effective for image-like data, to learn patterns within the spectrograms.
- Training & Evaluation: Training the CNN on a labeled music dataset and evaluating its performance in classifying genres.
Significance:
Music genre classification has wide applications, from improving music recommendation systems to content organization in large audio libraries. This project demonstrates an effective approach using modern deep learning techniques to tackle this challenging audio processing task.