Treble SDK for Machine Learning & AI Development
How the Treble SDK Transforms AI Development
The Treble SDK provides a cloud-powered simulation engine that allows ML teams to test and train models in diverse acoustic conditions without real-world constraints. It enables:
- High-fidelity simulations to evaluate ML model performance in various environments.
- Device-specific post-processing to simulate real-world playback conditions.
- Scalable batch processing for training AI models across thousands of scenarios.
- Integration with Python workflows for seamless automation and third-party tool compatibility.
By leveraging synthetic audio data from Treble, ML engineers achieve higher accuracy, better generalization, and faster deployment of AI-driven audio applications.
SDK feature summary
Go to our SDK pricing page for a full list of features.
Simulation at scale
Set up and launch thousands of advanced acoustic simulations in complex environments with a few Python commands.
Advanced source modeling
Model complex sound sources such as loudspeakers and the human voice via directional point sources and surface sources.
Spatial audio
Output physically accurate ambisonics room impulse responses up to 32nd order and render binaural / multi-channel output for auralization.
Device modeling
Model microphone arrays and listeners and render device-specific output in post-processing.
Scene generation
Import your own, leverage Treble’s enormous scene database or program-matically set up complex acoustic environments.
Machine learning workflows
Efficiently train and evaluate any kind of ML-based algorithm, e.g., speech enhancement and blind room estimation.
Real-time collaboration
Modern collaborative workflows, in-product support and shared assets such as materials, sources, receivers and sounds.
Automated workflows
Integrate the SDK into your custom workflows and connect with 3rd party tools for automated development and prototyping.
Machine Learning Validation of models using treble SDK
Treble SDK enables high-fidelity simulations, automated testing, and virtual prototyping, replacing costly physical measurements. Its Python-based interface streamlines ML evaluation, accelerating development and improving accuracy.
Source distance estimation
A source distance estimation model trained with Treble’s wave-based simulation outperforms a simpler approach, achieving higher accuracy and lower errors in complex acoustic environments.
Speech enhancement, recognition and separation
Discover how a wave/GA hybrid RIR dataset boosts ML performance in speech enhancement, recognition, and separation, outperforming GA-only datasets.
