Synthetic data enables complete control over acoustic conditions, perfectly labelled outputs, and rapid generation of scenarios that would be impractical or impossible to record.
The Challenge
Your models need more than clean audio
Speech recognition, enhancement, separation, and sound sensing depend on how audio reaches the microphone. Room acoustics, competing speakers, background noise, movement, and device geometry all shape the signals your models receive.
Capturing that diversity through recordings alone is slow and expensive. Existing datasets rarely match every device or deployment condition, leaving gaps in training and uncertainty about performance. Teams need a way to create targeted data and investigate difficult scenarios without organising another recording campaign.
How Treble Helps
Close the gaps in your training and testing data
Treble replaces expensive recording campaigns with scalable physics based simulation, enabling engineering teams to generate realistic datasets, validate machine learning models, and accelerate development through automated workflows.
Treble lets you create acoustic datasets around the environments, devices, and listening conditions that matter to your application. Control scene geometry, materials, sources, and microphone configurations to generate labelled audio with detailed metadata.
Complement real recordings with targeted synthetic data, including edge cases that are difficult to capture at scale. Evaluate model versions under repeatable conditions, isolate the factors behind failures, and use the results to guide your next training iteration.
How Treble Works
Simulate, Generate, and Validate
Turn your target use cases into controlled acoustic scenarios, create the data your models need, and evaluate performance before deployment.
Simulate
Define what your model needs to handle
Set up environments, acoustic materials, sources, and microphone configurations. Recreate intended listening conditions and introduce variations that challenge your system.
Generate
Build data around the gaps
Generate labelled audio across your chosen scenarios. Expand coverage of underrepresented conditions, create difficult examples, and augment existing recordings with data tailored to your application.
Validate
Understand where performance breaks down
Run the generated audio through your evaluation pipeline and compare model versions under the same conditions. Investigate failures, identify gaps in training coverage, and complement testing on real recordings.
Key Capabilities
Inside Treble
Better Design. Better Listening.
Lower Word Error Rate
0%
Lower source distance estimation error
0%
Lower source localization error
0%
Resources and validations
Improving multichannel speech enhancement through accurate room-acoustic simulations
Training on the high-fidelity dataset results in an up to 38% relative reduction in median word error rate compared to the lower-fidelity alternatives. These results show that augmentation with high-fidelity room-acoustic simulations directly translates into improved multichannel speech enhancement performance.
ML model evaluation using synthetic acoustic data
By evaluating against synthetic acoustic datasets with known parameters, you can assess algorithm performance systematically across a wide range of conditions without physical measurement.
Speech enhancement, recognition and separation
We demonstrate the higher accuracy of our IRs by comparing with recorded IRs from complex real-world environments.
Frequently Asked Questions
Yes. Treble provides a Python based interface that integrates directly into existing machine learning workflows and automation pipelines.
Treble supports workflows for speech enhancement, source localization, blind room estimation, denoising, adaptive audio, conversational AI, foundation models, and many other audio AI applications.
