IEEE SPS @ UF

Aude — biologically plausible audio scene analysis

Research on source separation and sound localization in real-world environments using informed models and a multi-microphone capture pipeline.

Research / IEEE SPS projects / Aude — biologically plausible audio scene analysis
Back to IEEE SPS projects GitHub
Aude project logo.

Overview

Aude develops an end-to-end audio research workflow: collect synchronized microphone-array data, benchmark state-of-the-art baselines, and train models for robust source separation and localization.

Aude is focused on machine listening in realistic environments where multiple sound sources overlap. The project combines hardware-informed data capture and model design to improve both source separation quality and spatial localization accuracy.

What I built

  • A project plan for reproducible baseline evaluation on source separation and localization tasks.
  • An initial hardware/data strategy using a 3-microphone capture setup with embedded collection components.
  • A milestone-driven workflow for moving from literature review to demoable, publishable results.

Snapshot

Track
IEEE SPS @ UF · audio ML · ICASSP track
Status
Active; baseline and data-capture phase in progress.
Focus
Source separationLocalizationMulti-mic data capture

Stack

  • Python
  • Audio DSP + machine learning
  • Microphone array + ESP32 capture hardware

Glossary

Source separation
Separating mixed audio into individual underlying sources (for example, different speakers or instruments).
Localization
Estimating where a sound source is located relative to the microphones.