Music Genre Classification Project Guide

Organizing massive music libraries manually by genre is time-consuming and subjective. A smart system that can listen to a piece of music and predict its genre accurately has tremendous applications for music streaming services, recommendation engines, and playlist automation. Automatic music genre classification involves analyzing musical patterns, rhythms, and spectral features, presenting an exciting challenge combining signal processing and machine learning expertise.

Music signals are transformed into visual spectrograms capturing time-frequency features, which are then fed into Convolutional Neural Networks (CNNs) for genre classification. Pre-trained audio models, fine-tuning, and spectrogram augmentation techniques enhance performance even with limited datasets. By training the system on diverse musical styles like rock, jazz, classical, hip-hop, and pop, it learns subtle patterns in beats, melodies, and energy levels that define each genre.

The system converts audio tracks into spectrogram images capturing sound frequency information over time. These spectrograms are input into CNN models trained to classify tracks into different genres. Feature extraction from time-domain (tempo, rhythm) and frequency-domain (harmonics, energy) enriches the model. The final output predicts the genre, offering applications in playlist management, auto-tagging, and music streaming personalization.

• Collect music datasets labeled by genre, like GTZAN or FMA datasets.
• Preprocess audio into Mel-spectrograms or MFCC (Mel Frequency Cepstral Coefficient) features.
• Build a CNN model for spectrogram classification or fine-tune pre-trained audio models.
• Evaluate using metrics like Precision, Recall, F1-Score, and Confusion Matrix analysis.
• Deploy as a web or desktop app where users can upload music files and get instant genre predictions.

• GTZAN Genre Dataset
• Librosa Audio Processing Library
• TensorFlow Audio Recognition Tutorials