RSK World - Music Classification Dataset | Genre Classification | Audio ML | Music AI | RSK World

Music Classification Dataset - RSK World

Music Classification Genre Classification Machine Learning 1,000+ Files Python TensorFlow Ready

This project features a comprehensive Music Classification dataset designed for professional music genre classification, music information retrieval, and audio analysis applications. The dataset includes 1,000+ labeled audio files across 8 genres (Classical, Jazz, Rock, Pop, Hip-Hop, Electronic, Country, Blues), preprocessed features (MFCC, spectral centroid, chroma, tempo), and Python scripts for model training. Includes powerful Python scripts: audio_processor.py for audio loading, feature_extractor.py for feature extraction (MFCC, spectral features), train_model.py for Random Forest/SVM/KNN model training, and example_usage.py for quick start examples. The package includes interactive demo website with audio player, analytics dashboard, comprehensive README.md, and MIT License. Perfect for data scientists, researchers, students, and developers working on music classification, genre recognition, music recommendation systems, and audio analysis projects.

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Dataset Overview

Complete music classification dataset with 1,000+ labeled audio files across 8 genres for music AI and machine learning.

1,000+ labeled audio files
8 music genres (Classical, Jazz, Rock, Pop, Hip-Hop, Electronic, Country, Blues)
30 seconds per sample
WAV and MP3 formats
22 kHz sample rate
Pre-labeled with genre tags
Balanced genre distribution
High-quality audio recordings
Training/test split (80/20)
Pre-extracted features available
Perfect for music classification & ML training

Dataset Structure & Files

Well-organized folder structure with audio files organized by genre, features, and metadata.

data/audio/ - Audio files organized by genre (WAV/MP3)
data/features.csv - Pre-extracted features
data/train_data.csv - Training dataset
data/test_data.csv - Test dataset
utils/ - Python utilities (audio_processor, feature_extractor)
notebooks/ - Jupyter notebooks
models/ - Trained models (train_model.py, predict.py)
Consistent naming convention
Easy to load with librosa
TensorFlow/PyTorch ready format
MFCC and spectral features preprocessed

Machine Learning Training

Complete training pipeline with support for Random Forest, SVM, KNN models and deep learning frameworks.

Random Forest classifier
SVM (Support Vector Machine)
KNN (K-Nearest Neighbors)
Neural Network support
TensorFlow/Keras support
PyTorch compatibility
MFCC feature extraction
Spectral features extraction
Batch processing support
Model checkpointing
Performance metrics report
Hyperparameter tuning
Model export & persistence

Multiple File Formats

Dataset available in multiple formats for maximum compatibility with different audio processing tools and frameworks.

WAV format (uncompressed audio)
MP3 format (compressed audio)
CSV format for metadata and features
Librosa compatible
NumPy array format
Easy format conversion
22 kHz sample rate
Standard audio formats
Feature files in CSV/NumPy
Compatible with all audio libraries
Pandas DataFrame ready

Analysis & Visualization

Comprehensive analysis tools with visualization capabilities and interactive music explorer.

Interactive Music Explorer
Genre distribution charts
Audio waveform visualization
Spectrogram visualization
MFCC feature plots
Feature correlation analysis
Performance benchmarking
Model comparison tools
HTML report generation
Export visualization images
Analytics Dashboard

Compatible Frameworks

Works with all major audio AI and machine learning frameworks out of the box.

TensorFlow/Keras
PyTorch deep learning
Librosa audio processing
NumPy numerical computing
scikit-learn ML library
pandas data manipulation
matplotlib visualization
Jupyter Notebook support
Random Forest, SVM, KNN
CNN for spectrograms
Neural Network models

What You Get

Complete package with all files needed for professional music classification projects.

1,000+ labeled audio files
Python utility scripts
audio_processor.py - Audio processing
feature_extractor.py - Feature extraction
train_model.py - Model training
predict.py - Prediction script
example_usage.py - Quick start
Jupyter notebook exploration
Interactive demo website
Audio player integration
Feature extraction pipeline
Complete documentation

Interactive Demo Website

Beautiful demo website with music explorer, live audio player, analytics dashboard, and comprehensive guide.

Modern animated design
Interactive Music Explorer
Live Audio Player
Analytics Dashboard
Filter by genre
Waveform visualization
Genre distribution charts
Performance metrics display
Step-by-step usage guide
Dark theme with gradients
Fully responsive layout

Python Scripts Included

Professional Python scripts for audio processing, feature extraction, and model training.

audio_processor.py - Audio loading & processing
feature_extractor.py - MFCC & spectral feature extraction
train_model.py - Random Forest/SVM/KNN model training
predict.py - Genre prediction script
example_usage.py - Quick start examples
audio_augmentation.py - Audio augmentation
model_comparison.py - Model evaluation
neural_network_model.py - Neural network training
Batch processing support
Feature extraction pipeline
Model training utilities
Complete code examples

Dataset Features

Comprehensive music dataset with 8 genres and various audio characteristics.

8 music genres - Classical, Jazz, Rock, Pop, Hip-Hop, Electronic, Country, Blues
Diverse musical styles - Robust training
Various artists - Generalizable models
Balanced genre distribution
30-second samples - Consistent format
High-quality recordings
Accurate genre labels
Pre-extracted features
Training/test split included
Easy to extend dataset
Total: 1,000+ audio files