ELEC-E5500 - Speech Processing, Lecture, 8.9.2022-17.10.2022

Topic outline

• Exercises
  • 

    Exercise 1: Basics of speech processing Assignment

    The goal of this exercise is to get acquainted with basics of speech processing. This includes recording, reading, resampling, windowing, and computing magnitude spectrum and spectrogram along with visualizations.

  • 

    Exercise 2: Fundamental frequency estimation Assignment

    In this exercise, you will be implementing two popular fundamental frequency estimation methods: They are: (1) auto-correlation and (2) cepstral methods.

    
    Please read carefully, the instructions provided in the Jupyter notebook.
    

  • 

    Exercise 1: Solution Folder

    Not available unless: You belong to any group
  • 

    Exercise 3: Voice Activity Detection Assignment

    In this exercise, you will be implementing functions to extract simple speech features which are suitable for voice activity detection (VAD) and utilize them to train two simple VAD classifiers. The computed features are combined into a matrix that acts as the input data provider for our classifiers. The code for the classifier training and some features are already provided in the notebook, but you must experiment with different features and properties, and report your findings.

    
    

  • 

    Exercise 2: Solution Folder

    Not available unless: You belong to any group
  • 

    Exercise 4: Speech Enhancement and Evaluation Assignment

    The goal of this exercise is to implement basic speech enhancement techniques and evaluate and visualize the quality of the enhancement. You will be implementing four different methods: Spectral-subtraction, Wiener-filter, linear-filter and a VAD based filter.

    
    

  • 

    Exercise 3: Solution Folder

    Not available unless: You belong to any group
  • 

    Exercise 4: Solution Folder

    Not available unless: You belong to any group