Thesis Repo

This project is just a repository to store changes made to my dissertation in the ENES UNAM Morelia, Mexico.

Document Structure

• Title Page

• Table of Contents

• Acknowledgements

• Abstract

• Preface

• Chapter 0 (Introduction)

  • The Cocktail Party Problem
  • Historic Background
    • Segmentation vs Attention Problem
    • Inverse Problems
    • Ill-Posed Problems
    • Constraints
  • Structure of Document

Part 1 (Literature Review)

• Chapter 1 (Data Processing)
  • Feature Extraction
    • Spectrograms
    • Mel-Bin
  • Normalization
• Chapter 2 (The Generation Problem)
  • Time Series
    • Wave-net
  • Phase Loss
    • Phase Storage
    • Griffin-lin
    • Vocoders
  • Masking Techniques
• Chapter 3 (Looking to Listen)
• Chapter 4 (Music-Speech)

Part 2 (Methodology)

• Chapter 5 (Libraries)
  • Librosa
  • PyTorch
  • TorchAudio
• Chapter 6 (Implementation)
  • Dataset
  • Model Structure
  • Preprocessing
  • Targets (Tested)

Part 3

• Chapter 7 (Results)
  • Results
    • Quantified Losses
    • Quality Tests
    • Samples (images and audios)
  • Transfer Learning
    • Quantified Losses
    • Quality Tests
    • Samples (images and audios)
  • Transfer Learning with fine tuning
    • Quantified Losses
    • Quality Tests
    • Samples (images and audios)
• Chapter 8 (Discussion)
  • Future Work

Appendix

• Time Series

  • Signal Processing
  • Audio/Speech Properties

• Transforms

  • Fourier Transform
    • Spectrums
    • Discrete Fourier Transform
    • Short-Time Fourier Transform
  • Wavelet
    • Scaleograph

• Spectrograms

  • Linear and Log
    • Amplitude vs Decibels
    • Mel-Bins
  • The Phase Problem
    • Linear
    • Decibel
    • Mel-Bin
  • Phase Retrieval Techniques
    • Phase Storage
    • Griffin-lin Algorithm
    • Vocoders

• Machine Learning

  • Supervised Learning vs Unsupervised Learning
  • Advancements
  • Problems
    • Data
    • Interpretability
    • Overfitting
    • Hyper-parameters
    • Computation

• Deep Learning

  • Neural Networks
    • Structure
      • Inputs
      • Weights
      • Bias
    • Back-propagation Algorithm
      • Forward Propagation
      • Gradients
  • Image processing
    • Convolutional Neural Networks
    • Attention
  • Segmentation
    • Medicine
    • U-Net

• Source Code

  • Running Instructions