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Example Jupyter Notebooks and sample data put together for learning and presentation
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00_Getting_Started.ipynb
01_Data_Exploration.ipynb
02_Data_File_Preprocessing.ipynb
03_Audio_Data_Model_MFCC.ipynb
04_Audio_Data_Model_Chromagram.ipynb
05_Transcription.ipynb
06_Keyword_Extraction.ipynb
07_Keyword_Spotting.ipynb
08_Topic_Modeling.ipynb
09_Sentiment_Analysis.ipynb
LICENSE
README.md
requirements.txt

README.md

Audio Data Insights

Example Jupyter Notebooks and sample data put together for learning and presentation.

Digital Audio data contains sounds that have been recorded and converted into digital form. One example is conversation and user interactions with voice applications. The code in this repo explores different types of features that you can use when working to extract features and information from audio data. Further, we These sample notebooks were built to use for demos and learning how to work with audio data.

The related presentation slide deck can be found here.

Running the Notebooks

  1. Fork and Pull this Repo

  2. Change Directory into your local version of this Repo.

  3. Install Python 3.6 or later.

  4. Create a Virtual Environment: python3 -m venv <path-to-venvs\env_name>

  5. Actiivate the Virtual Environment: source <path-to-venvs\env_name>\bin\activate

  6. Install Jupyter Lab: pip install jupyterlab

  7. Install Project Requirements: pip install -r requirements.txt

  8. Start Jupyter Lab jupyter lab

Meta

Ryan Bales – @ryanbalesryan@balesofdata.com

Distributed under the Apache 2.0 license. See LICENSE for more information.

https://github.com/ryanbales/

Contributing

  1. Fork it (https://github.com/ryanbales/Audio-Data-Insights/fork)
  2. Create your feature branch (git checkout -b feature/fooBar)
  3. Commit your changes (git commit -m "<comments>")
  4. Push to the branch (git push origin feature/fooBar)
  5. Create a new Pull Request
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