Project Overview
This machine learning project demonstrates deep learning expertise by building a Convolutional Neural Network (CNN) to classify music by artist using mel spectrogram audio representations. The project showcases a complete ML pipeline from data processing to model training, converting raw audio files into visual representations optimized for neural network analysis.
Related ML Projects
This project is part of a broader portfolio of machine learning applications, including:
- Student Stress Prediction: Multi-model ML system using Neural Networks, Random Forests, and Support Vector Machines
- Lung Disease CNN: Deep learning classification on 8000+ medical images from Kaggle
- YouTube Video Summary AI: NLP application for automated video content summarization
- DQN Reinforcement Learning: PyTorch-based reinforcement learning for CartPole control problem
Key Features
- Audio Processing: Converts MP3 audio files to mel spectrograms using librosa library
- Data Pipeline: Implements automated data loading and preprocessing from multi-level directory structure
- Data Visualization: Uses Plotly for interactive visualization of audio features
- Deep Learning: Builds CNN model using Keras/TensorFlow for artist classification
- Scientific Computing: Utilizes NumPy and Pandas for data manipulation and analysis
Technology Stack
Python
TensorFlow
Keras
NumPy
Pandas
Librosa
Matplotlib
Plotly
What's Included
- Complete Jupyter notebook with documented code
- Data loading and preprocessing pipeline
- Audio feature extraction and visualization
- CNN model architecture and training
- Evaluation metrics and performance analysis
Skills Demonstrated
- Machine Learning & Deep Learning
- Audio Signal Processing
- Data Science Workflow
- Python Programming
- Data Visualization
- Neural Network Architecture Design