Task
The client had to trigger effects by hand during a performance. It distracted from the creative work, raised the risk of mistakes in real time and made it hard to focus on the show.
Solution
I built a neural network in PyTorch that predicts the right commands in real time. The network listens to the track and knows when an effect fits and which one, as well as the experienced DJ whose data it was trained on.
What was done
- Designed an ML pipeline in PyTorch from data collection to real-time inference
- Trained a neural network to predict the right effect (F1 above 0.8)
- Added streaming track processing with predictions during playback
- Integrated the model into the DJ software and removed manual triggering
Result
The neural network now runs the show in semi-automatic mode. The DJ supervises and focuses on the music and the audience, and the routine button pressing is gone.
Technologies
PythonPyTorchDjango
