Giving Voice to a Raven: Audio Analysis and Servo Control with Python
What if sound could drive movement? This talk introduces a Python-based program I developed that transforms audio into motion patterns - built for animatronics, optimized for embedded devices, and demonstrated through a raven named Samuel.
How do you get an animatronic raven to speak like a real one - complete with emotion, variation, and natural presence? In this talk, we'll explore how I built a Python-powered audio analysis system that brings Samuel, my interactive animatronic raven, to life. The goal wasn’t just to react to sound, but to create believable, nuanced motion driven by real audio data.
The challenge: to translate raven vocalizations into natural beak movements, without manually scripting the motion for each sound file. I needed a system that could analyze audio and generate movement instructions automatically - flexible, scalable, and Python-driven.
Using libraries like librosa, numpy, and matplotlib, I built a system that analyzes raven sounds, detects vocal energy peaks, and generates servo-ready movement instructions. To guide development, I created visual graphs of the audio - including RMS energy, volume, and frequency - to better understand how sound translated into motion. The program also generates multiple movement maps per sound clip, selecting one at random at runtime to avoid repetitive, robotic behavior.
We'll dive into: - Audio signal analysis with librosa (RMS, STFT, clustering). - Generating servo-ready binary movement patterns from raw audio. - Designing variability through randomized motion mapping. - Visualizing sound energy. - Controlling servos in real time using Python and Raspberry Pi.