TL;DR — The Treble SDK Scene Generator provides an interactive workflow for constructing, serialising, and rendering realistic multi-speaker and acoustic scenes from physically accurate room simulations. It decouples lightweight scene recipes from on-demand audio rendering to scale dataset generation for spatial audio and speech tasks.
Key contributions
- Introduces a Python SDK workflow for building complex audio scenes combining room-acoustic simulations, clean source audio, background noise, and device models.
- Implements a 'lazy' representation that separates lightweight scene recipes (tracks, RIR mappings, metadata) from heavy audio rendering (convolution and mixing).
- Supports both manual interactive scene design with timeline/3D-room visualisation and automated bulk generation of scene collections via rule objects.
- Integrates device and listener configurations including head-related device models, orientation, device noise, and filtering.
Problem
Modern speech and audio systems frequently operate in dynamic acoustic spaces like meeting rooms, vehicles, and homes, where signals depend heavily on room geometry, material properties, microphone/listener placement, and multi-speaker overlap. Collecting real recordings covering this combinatorial variability is expensive, hard to control, and difficult to scale. Prior approaches often rely on isolated room impulse responses or custom ad-hoc scripting, lacking unified pipelines to manage mixtures, separated targets, spatial audio parameters, and structured metadata together.
Method
The Treble SDK Scene Generator takes as input collections of room impulse responses (IRs), clean audio sources (such as public speech datasets and background noise like HVAC), and rule definitions. The architecture uses a decoupled design: during creation, a scene is stored as a lightweight recipe containing timed audio blocks, track-to-RIR mappings, listener configurations, and JSON-serialisable metadata without performing any convolution or mixing. When audio output is requested, the scene is rendered on demand by convolving each source track with its corresponding room and device response, followed by summing the components.
For manual construction, users define conversational structures, utterance durations, overlap conditions, level ranges, and spatial source coordinates via a Jupyter notebook interface. The listener is specified using a dummy head model equipped with orientation parameters, per-channel device noise, and optional filtering. For automated bulk generation, rule objects and source groups iterate over randomised receiver positions, speaker identities, timing patterns, and acoustic conditions to produce a SceneCollection.
Experimental setup
The system demonstrates integration using public Hugging Face speech datasets for content, background noise collections for ambient sounds, and pre-computed or simulated IR collections defining source-receiver paths. Evaluation and inspection are supported via timeline visualisations, 3D room plots, and metadata verification rather than comparative model benchmarking. The framework is implemented as an interactive Python Jupyter notebook workflow relying on the underlying Treble SDK engine.
Results
The paper presents a demonstration of the Scene Generator workflow rather than numerical machine learning benchmarks. It successfully constructs single and bulk multi-speaker conversational scenes with background noise and spatial listener orientations. The system enables lossless round-trip serialisation of scene metadata, precise timestamp tracking, and aligned target extraction for supervised machine learning pipelines.
Limitations
The framework relies heavily on the underlying physical accuracy and computational efficiency of the Treble SDK acoustic simulation engine. The paper details a demonstration workflow without reporting scaling limits regarding memory overhead for massive bulk generations, compute times for high-density multi-source scenes, or empirical validation of downstream model performance gains.
Why read this
Speech and ML engineers building pipelines for far-field recognition, spatial audio, or speech enhancement should read this to learn how to automate reproducible, physically grounded synthetic dataset creation without writing custom gluing scripts.
Code
None released (as of this page's updated date). If you are an author with a repo, please claim this entry — see CONTRIBUTING.md.
Applications
Training and evaluating robust far-field speech recognition, speech enhancement, speaker separation, and spatial audio systems in simulated environments.
Institutions
Treble Technologies
Related
- Improving Multichannel Speech Enhancement through Accurate Room-Acoustic Simulations — complementary · relatedness 2.1/3
- Mind the Gap: Impact of Synthetic Conversational Data on Multi-Talker ASR and Speaker Diarization — complementary · relatedness 2.0/3
- Explicit Context-Driven Neural Acoustic Modeling for High-Fidelity RIR Generation — complementary · relatedness 1.9/3
- Probing Spatial Structure in Pretrained Audio Representations — complementary · relatedness 1.8/3
- YODAS v3: Over 1 Million Hours of High-Bandwidth, Stereophonic, Multilingual Speech — complementary · relatedness 1.8/3
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