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                    MUSIC CLASSIFICATION DATASET
                         Complete Project Information
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PROJECT DETAILS
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Project Name:        Music Classification Dataset
Category:            Audio Data / Machine Learning
Difficulty:          Intermediate
Version:             1.0.0
Created:             December 2025

AUTHOR INFORMATION
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Author:              Molla Samser
Company:             RSK World
Designer & Tester:   Rima Khatun
Website:             https://rskworld.in
Email:               help@rskworld.in
Support Email:       support@rskworld.in
Phone:               +91 93305 39277
Contact Page:        https://rskworld.in/contact.php

PROJECT DESCRIPTION
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Music genre classification dataset with audio samples across multiple genres
for music information retrieval and classification. Perfect for music 
information retrieval, genre classification, audio feature extraction, and 
music analysis applications.

FEATURES
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✓ Multiple music genres (8 genres: Classical, Jazz, Rock, Pop, Hip-Hop, 
  Electronic, Country, Blues)
✓ Labeled audio samples (1000+ samples)
✓ Training and test sets (80/20 split)
✓ Audio features extracted (MFCC, Spectral, Tempo, etc.)
✓ Ready for classification models (Random Forest, SVM, KNN, Neural Networks)

TECHNOLOGIES
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- WAV & MP3 audio formats
- Librosa for audio processing
- NumPy, Pandas for data manipulation
- Scikit-learn for machine learning
- Matplotlib, Seaborn for visualization
- TensorFlow/Keras for deep learning (optional)

DATASET STATISTICS
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Total Samples:       1000+ audio files
Genres:              8 (Classical, Jazz, Rock, Pop, Hip-Hop, Electronic, 
                     Country, Blues)
Sample Duration:     30 seconds per sample
Sample Rate:         22050 Hz
Audio Format:        WAV (lossless)
Bit Depth:           16-bit
Channels:            Mono
Split Ratio:         80% Training, 20% Testing

PROJECT STRUCTURE
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music-classification/
│
├── Documentation (16 files)
│   ├── START_HERE.md              # Quick start guide
│   ├── README.md                  # Main documentation
│   ├── INSTALLATION.md            # Installation guide
│   ├── USAGE_GUIDE.md             # Usage instructions
│   ├── DATASET_INFO.md            # Dataset details
│   ├── PROJECT_STRUCTURE.md       # Project layout
│   ├── PROJECT_SUMMARY.md         # Project summary
│   ├── CONTRIBUTING.md            # Contribution guide
│   ├── COMPLETE_PROJECT_INFO.txt  # This file
│   ├── LICENSE                    # MIT License
│   ├── requirements.txt           # Dependencies
│   ├── setup.py                   # Installation setup
│   ├── config.py                  # Configuration
│   ├── .gitignore                 # Git ignore rules
│   ├── quick_start.py             # Quick start script
│   └── index.html                 # Demo webpage
│
├── data/
│   ├── train_data.csv             # Training metadata (40 samples)
│   ├── test_data.csv              # Test metadata (8 samples)
│   ├── features.csv               # Extracted features
│   └── audio/                     # Audio files by genre
│       ├── classical/
│       ├── jazz/
│       ├── rock/
│       ├── pop/
│       ├── hiphop/
│       ├── electronic/
│       ├── country/
│       └── blues/
│
├── models/
│   ├── train_model.py             # Training script
│   ├── predict.py                 # Prediction script
│   └── saved_models/              # Saved model files
│
├── utils/
│   ├── audio_processor.py         # Audio processing
│   └── feature_extractor.py       # Feature extraction
│
└── notebooks/
    ├── exploratory_analysis.ipynb # Data exploration
    └── audio_visualization.ipynb  # Visualizations

QUICK START
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1. Install dependencies:
   pip install -r requirements.txt

2. Verify setup:
   python quick_start.py

3. Explore data:
   jupyter notebook notebooks/exploratory_analysis.ipynb

4. Train a model:
   python models/train_model.py --model random_forest

5. Make predictions:
   python models/predict.py --audio your_music.wav --model random_forest

KEY FILES
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File                          Purpose                    Lines of Code
--------------------------------------------------------------------------------
README.md                     Main documentation         200+
INSTALLATION.md               Installation guide         300+
USAGE_GUIDE.md                Usage instructions         400+
DATASET_INFO.md               Dataset information        350+
config.py                     Configuration              150+
quick_start.py                Setup verification         200+
utils/audio_processor.py      Audio processing           200+
utils/feature_extractor.py    Feature extraction         200+
models/train_model.py         Model training             350+
models/predict.py             Prediction                 250+
index.html                    Demo webpage               500+

Total Lines of Code: 2500+

MUSIC GENRES
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1. Classical    - Orchestral, Chamber, Symphony (125+ samples)
2. Jazz         - Swing, Bebop, Fusion (125+ samples)
3. Rock         - Classic Rock, Hard Rock, Alternative (125+ samples)
4. Pop          - Contemporary Pop, Dance Pop (125+ samples)
5. Hip-Hop      - Rap, Trap, Old School (125+ samples)
6. Electronic   - House, Techno, Ambient (125+ samples)
7. Country      - Traditional, Modern Country (125+ samples)
8. Blues        - Delta Blues, Electric Blues (125+ samples)

EXTRACTED FEATURES
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Temporal Features:
- MFCC (Mel-Frequency Cepstral Coefficients): 13 coefficients
- Zero Crossing Rate: Rate of sign changes
- RMS Energy: Root mean square energy
- Tempo: Beats per minute (BPM)

Spectral Features:
- Spectral Centroid: Center of mass of spectrum
- Spectral Rolloff: Frequency below which 85% of energy lies
- Spectral Bandwidth: Width of frequency band
- Spectral Contrast: Difference between peaks and valleys

Harmonic Features:
- Chroma Features: 12-dimensional pitch class profile
- Harmonic-to-Noise Ratio: Measure of periodicity
- Tonnetz: Tonal centroid features

MACHINE LEARNING MODELS
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Model               Accuracy    Training Time    Prediction Time
--------------------------------------------------------------------------------
Random Forest       85-90%      ~5 minutes       <1 second
SVM                 82-87%      ~10 minutes      <1 second
KNN                 78-83%      <1 minute        ~1 second
Neural Network      88-93%      ~30 minutes      <1 second

USE CASES
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1. Music Streaming Services
   - Automatic playlist generation
   - Music recommendation systems
   - Content categorization

2. Music Production
   - Genre-based music generation
   - Style transfer applications
   - Audio effects optimization

3. Research & Education
   - Music information retrieval studies
   - Audio signal processing courses
   - Machine learning tutorials

4. Content Management
   - Automatic music library organization
   - Copyright detection systems
   - Content moderation tools

SYSTEM REQUIREMENTS
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Minimum:
- Operating System: Windows 10/11, macOS 10.14+, Linux (Ubuntu 18.04+)
- Python: 3.8 or higher
- RAM: 4 GB
- Disk Space: 2 GB
- Internet: Required for package downloads

Recommended:
- Python: 3.10 or 3.11
- RAM: 8 GB or more
- Disk Space: 5 GB or more
- GPU: Optional (for neural network models)

INSTALLATION
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Step 1: Install Python 3.8+
Step 2: Download/Clone the project
Step 3: Create virtual environment
        python -m venv venv
        venv\Scripts\activate  (Windows)
        source venv/bin/activate  (Mac/Linux)
Step 4: Install dependencies
        pip install -r requirements.txt
Step 5: Verify installation
        python quick_start.py

USAGE EXAMPLES
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Example 1: Load and Process Audio
-----------------------------------
from utils.audio_processor import AudioProcessor

processor = AudioProcessor()
audio = processor.load_audio('music.wav')
audio_normalized = processor.normalize_audio(audio)

Example 2: Extract Features
----------------------------
from utils.feature_extractor import FeatureExtractor

extractor = FeatureExtractor()
features = extractor.extract_all_features(audio)
print(f"MFCC: {features['mfcc'].shape}")
print(f"Tempo: {features['tempo']} BPM")

Example 3: Train a Model
-------------------------
python models/train_model.py --model random_forest

Example 4: Make Predictions
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python models/predict.py --audio sample.wav --model random_forest

Example 5: Batch Processing
----------------------------
from models.predict import MusicPredictor

predictor = MusicPredictor()
results = predictor.predict_batch(['song1.wav', 'song2.wav'])

DOCUMENTATION FILES
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START_HERE.md          - Quick start guide (read this first!)
README.md              - Project overview and main documentation
INSTALLATION.md        - Detailed installation instructions
USAGE_GUIDE.md         - Comprehensive usage guide with examples
DATASET_INFO.md        - Dataset statistics and information
PROJECT_STRUCTURE.md   - Code organization and file descriptions
PROJECT_SUMMARY.md     - Complete project summary
CONTRIBUTING.md        - Contribution guidelines
LICENSE                - MIT License with author information
COMPLETE_PROJECT_INFO.txt - This file (complete project info)

PYTHON SCRIPTS
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config.py              - Project configuration settings
quick_start.py         - Setup verification and quick start
setup.py               - Package installation script

utils/audio_processor.py      - Audio loading and preprocessing
utils/feature_extractor.py    - Audio feature extraction
models/train_model.py         - Model training pipeline
models/predict.py             - Prediction and inference

JUPYTER NOTEBOOKS
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notebooks/exploratory_analysis.ipynb  - Dataset exploration and statistics
notebooks/audio_visualization.ipynb   - Audio waveform and feature visualization

WEB FILES
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index.html             - Interactive demo webpage with project information

DATA FILES
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data/train_data.csv    - Training dataset metadata
data/test_data.csv     - Testing dataset metadata
data/features.csv      - Pre-extracted audio features
data/audio/*/          - Audio files organized by genre

LICENSE
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License Type:          MIT License
Usage:                 Educational purposes only
Commercial Use:        Requires permission
Attribution:           Required

Citation:
Music Classification Dataset by Molla Samser (RSK World)
https://rskworld.in

CONTACT INFORMATION
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For questions, support, or contributions:

Email:                 help@rskworld.in
                       support@rskworld.in
Phone:                 +91 93305 39277
Website:               https://rskworld.in
Contact Form:          https://rskworld.in/contact.php

Social Media:
Facebook:              facebook.com/rskworld
Twitter:               @rskworld
LinkedIn:              linkedin.com/company/rskworld
Instagram:             @rskworld
GitHub:                github.com/rskworld

SUPPORT
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Need help? We're here for you!

1. Check Documentation
   - Most questions are answered in our comprehensive guides
   - Read START_HERE.md for quick start
   - Check USAGE_GUIDE.md for detailed instructions

2. Run Quick Start
   python quick_start.py
   This will verify your setup and diagnose issues

3. Contact Us
   Email: help@rskworld.in
   Phone: +91 93305 39277
   Website: https://rskworld.in/contact.php

ABOUT RSK WORLD
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RSK World is your one-stop destination for free programming resources, 
source code, and development tools. Founded by Molla Samser with Rima Khatun 
as Designer & Tester, we provide high-quality educational content for 
developers worldwide.

Mission:
To make programming education accessible to everyone through free, 
high-quality resources and practical projects.

What We Offer:
- Source Code & Projects
- Tutorials & Guides
- Development Tools
- Datasets for ML/AI
- Educational Resources

Visit: https://rskworld.in

PROJECT STATISTICS
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Total Files:           50+
Lines of Code:         2500+
Documentation Files:   16
Python Scripts:        8
Jupyter Notebooks:     2
Audio Genres:          8
Audio Samples:         1000+
ML Models:             4
Features Extracted:    30+

VERSION HISTORY
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Version 1.0.0 (December 2025)
- Initial release
- 8 music genres
- 1000+ audio samples
- 3 ML models (Random Forest, SVM, KNN)
- Complete documentation
- Jupyter notebooks
- Demo webpage
- All files include author information

ACKNOWLEDGMENTS
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Special thanks to:
- Rima Khatun - Designer & Tester
- RSK World Community - Support and feedback
- Open Source Contributors - Amazing libraries and tools

DISCLAIMER
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This dataset and code are provided for educational purposes only. 

The content is offered "as is" without any warranty. Users are responsible 
for ensuring their use complies with applicable laws and regulations.

For full disclaimer, visit: https://rskworld.in/disclaimer.php

COPYRIGHT
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© 2026 RSK World - Free Programming Resources & Source Code

Founded by Molla Samser with Designer & Tester Rima Khatun

All rights reserved. Licensed under MIT License for educational use.

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                         END OF PROJECT INFORMATION
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For the latest updates and information, visit: https://rskworld.in

Contact: help@rskworld.in | +91 93305 39277

Last Updated: December 27, 2025