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Three-Week Plan: Audio Data Analysis and modelling

Week 1: Data Preprocessing and Exploration

Week 2: Model with Classical ML Algorithms

Week 3: Model with Neural Networks

Week 1: Data Preprocessing and Exploration

  • Exploration of both multiple data types.
  • Performing pre-preprocessing steps on different data types, including cleaning, feature engineering, and feature extraction.

Assignment Tasks:

  1. Download and try some examples of RAVDESS audio speech emotion data.
  2. Create your example emotional speech for all of the emotion caterogies by recording your voice. Try to use a similar setting/sentence length with the RAVDESS audio examples.
  3. Add your new audio files to your own OneDrive with a shared link for us to access. Make sure we have "view" permissions. Copy this link for your report.
  4. Combine your voice data with the rest of RAVDESS, by making sure you follow the file naming conventions.
  5. Clone the jupyter notebook into your repository on github.
  6. Perform the same analysis on the combined dataset. [Feature analysis and cleaning, write about the differences between RAVDESS and your dataset]
  7. Commit the changes in your repository, if any.

Write a 1–page report on the analysis that you have performed with the combined dataset, add images. Add a link to your github and shared dataset on OneDrive. Make sure we can access/view them. You can include issues you encountered, how did you solve problems. Upload your report on canvas in PDF format. [between 500-1000 word limit] Cite all your resources, including the datasets as references (outside of page/word limit . Credits: https://github.com/IliaZenkov/sklearn-audio-classification

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