Neural Network Trained on Campfire Audio
Neural Network Trained on Campfire Audio
This video demonstrates using TensorFlow WaveNet to train a neural network on campfire audio samples. The model learns the patterns and characteristics of campfire sounds and can generate new audio based on what it learned.
What is WaveNet?
WaveNet is a deep neural network for generating raw audio waveforms, originally developed by DeepMind. It can:
- Learn the characteristics of any audio
- Generate new audio that sounds similar to training data
- Capture complex patterns in sound waves
- Framework: TensorFlow
- Model: WaveNet architecture
- Repository: https://github.com/ibab/tensorflow-wavenet
- Bird sounds
- Waterfalls
- Rain
- Ocean waves
- Any ambient audio
The Project
Training Data
The neural network was trained on recordings of campfire audio - the crackling, popping, and ambient sounds of burning wood.
The Process
1. Collect campfire audio samples
2. Preprocess audio for the neural network
3. Train the WaveNet model on the samples
4. Generate new campfire sounds from the trained model
Results
The trained model can produce synthesized campfire audio that captures the essence of the original recordings - the random crackles, the low rumble, the organic nature of fire sounds.
Technical Details
Future Ideas
The same technique could be applied to:
Let me know if you want to see experiments with other natural sounds!