Build a Deep Audio Classifier with Python and Tensorflow

Learn to build a Deep Audio Classification model using Python and TensorFlow in this comprehensive tutorial. Begin with a client call and project breakdown, then progress through installing dependencies and creating data loading functions. Explore TensorFlow dataset creation, determine average call lengths, and build preprocessing functions. Create training and testing partitions before constructing a Deep CNN model for audio clip classification. Develop a forest parsing function, predict results for all files, and export findings to CSV. Follow along with provided code and data sources, and engage in hands-on missions throughout the tutorial to reinforce learning.

- START
- CLIENT CALL 1
- Breakdown Board
- MISSION 1
- Install and Import Dependencies
- Build a Dataloading Function
- MISSION 2
- Create Tensorflow Dataset
- Determine Average Call Length
- Build Preprocessing Function
- MISSION 3
- Create Training and Testing Partitions
- Build Deep CNN Model
- Classifier Audio Clips
- MISSION 4
- Build Forest Parsing Function
- Predict All Files
- MISSION 5
- Export Results to CSV