Audio Signal Processing For Music Applications

⚠️ Academic Integrity Notice: This repository contains my personal work and solutions for the Audio Signal Processing for Music Applications course at MTG-UPF. It is shared for portfolio purposes only. If you are currently enrolled in this course, please note that viewing or copying these solutions would violate academic integrity policies. Please develop your own solutions to the assignments.

This repository contains a comprehensive collection of assignments and projects completed during the Audio Signal Processing for Music Applications course at MTG-UPF MSc Sound And Music Computing program. The work showcases various aspects of digital signal processing applied to music and audio analysis.

🎯 Project Overview

This portfolio demonstrates practical implementations of key audio processing concepts including:

• Basic audio operations in Python
• Discrete Fourier Transform (DFT) and its properties
• Short-time Fourier Transform (STFT)
• Sinusoidal and Harmonic modeling
• Sound transformations and creative audio processing
• Machine learning applications in sound description

🚀 Getting Started

Prerequisites

• Python 3.7+
• Jupyter Notebook
• Required Python packages (see requirements.txt)

Installation

1. Clone the repository:

git clone https://github.com/yourusername/Audio-Signal-Processing-For-Music-Applications.git
cd Audio-Signal-Processing-For-Music-Applications

2. Install dependencies:

pip install -r requirements.txt

📁 Project Structure

The repository is organized into weekly assignments, each focusing on different aspects of audio signal processing:

1. Week 1 - Python and sounds: Introduction to basic audio operations using Python
2. Week 2 - Sinusoids and DFT: Understanding basic elements and operations of the Discrete Fourier Transform
3. Week 3 - Fourier properties: Exploration of Fourier theorems and DFT properties
4. Week 4 - STFT: Implementation of Short-time Fourier Transform and onset detection
5. Week 5 - Sinusoidal model: Analysis and tracking of sinusoids in audio signals
6. Week 6 - Harmonic model: Fundamental frequency estimation and harmonic analysis
7. Week 7 - Sinusoidal plus residual model: Analysis and synthesis using the Harmonic plus Stochastic model
8. Week 8 - Sound transformations: Creative sound transformations using the HPS model
9. Week 9 - Sound and music description: Machine learning applications for sound description

📝 Usage

Each notebook (.ipynb file) is self-contained with detailed explanations and implementations. To run a notebook:

1. Navigate to the desired notebook
2. Launch Jupyter Notebook:

jupyter notebook

3. Open the notebook and run the cells sequentially

📄 License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details. This strong copyleft license helps ensure that derivatives of this code must also be open source, which aligns with academic principles of knowledge sharing while protecting against misuse.

🤝 Acknowledgments

• MTG-UPF Master's in Sound and Music Computing program
• Course instructors and teaching assistants
• Music Technology Group at Universitat Pompeu Fabra