Simulation enables repeatable testing across thousands of acoustic conditions that would be impractical or impossible to capture through physical measurements.
Treble for Robotics & Physical AI
Give physical AI the ability to truly hear
Develop audio hardware and AI for robots that interact with people and their surroundings. Explore microphone designs, generate representative training data, and evaluate performance through physics accurate acoustic simulation.
The Challenge
Teaching machines to understand sound is difficult.
Robots need to understand spoken instructions, locate speakers, and recognise sounds in environments that rarely stay quiet or predictable. Distance, competing conversations, background noise, and reflections all affect what their microphones capture. As a robot moves or turns, those listening conditions change again.
Physical prototypes and recording campaigns are slow and resource intensive, and rarely cover the range of situations a robot will encounter. Teams need to understand how hardware and algorithms perform together, including in unfamiliar environments and challenging edge cases, before deployment.
How Treble helps
Bring real world acoustics into robot development
Treble lets you model robot geometry, compare microphone configurations, and simulate how sound travels through the surrounding environment to each microphone. Investigate how device shape, placement, and orientation influence what the system hears before committing to hardware.
Create controlled scenes with speech, noise, and varied source and receiver positions. Generate labelled audio for training and evaluation, compare designs under repeatable conditions, and investigate failures without recreating every scenario in a physical lab.
Develop intelligent systems that understand their acoustic environment.
Built for robotics and embodied AI
Connect hardware design and audio AI development in one workflow. Understand what your robot captures, create data for its intended tasks, and assess performance across the conditions it will encounter.
Design how your robot listens.
Microphone & Array Design
Explore microphone positions and array layouts on your robot. Account for acoustic shadowing and scattering from its geometry, and compare configurations before building physical prototypes.
Make voice interaction work beyond the demo.
Voice Interaction
Generate scenarios with people speaking from different distances, directions, and orientations. Develop and evaluate beamforming, speech enhancement, and recognition systems in the presence of competing voices and reverberation.
Train perception around your robot.
Device Specific Training Data
Create labelled acoustic datasets tailored to your robot’s geometry, microphone configuration, and intended environments. Use your source recordings to generate varied examples for speech processing, sound recognition, and source localisation.
Find the situations that challenge your system.
Real World Acoustic Complexity
Explore changing source and receiver positions, background noise, obstructions, and complex reflections. Test difficult combinations systematically to identify where audio algorithms struggle and where more training data is needed.
Purpose built for robotics workflows
Inside Treble
Validated so you can develop with confidence
Improving multichannel speech enhancement through accurate room-acoustic simulations
How much does acoustic fidelity matter for model training? In this study, models trained with high-fidelity simulated acoustic data achieved up to a 38% relative reduction in median word error rate compared with lower-fidelity alternatives, when evaluated on measured data.
Synthesis of Room Acoustics for Speaker Distance Estimation
Explore a benchmark for generating room acoustic data and evaluating its value for estimating a speaker’s distance from a microphone.
Room-Acoustic Simulations as an Alternative to Measurements for Audio-Algorithm Evaluation
See how simulated acoustic data compares with measurements when evaluating audio algorithms, and how simulation fidelity influences the results.
Frequently Asked Questions
Yes. Treble supports microphone arrays, custom device geometries, and robot specific acoustic configurations for realistic sensor evaluation.
Yes. Treble can generate perfectly labelled synthetic datasets with configurable environments, moving sources, background noise, and device characteristics to support machine learning development.
