ML model evaluation using synthetic acoustic data
Evaluating audio ML model performance against recorded data is costly, slow, and difficult to reproduce across hardware configurations. Physics-accurate simulation offers a controlled alternative: by evaluating against synthetic acoustic datasets with known parameters, you can assess algorithm performance systematically across a wide range of conditions without physical measurement.
A good evaluation set is different from a good training set. Training data benefits from acoustic diversity. An evaluation set should be controlled and intentional, so that evaluation results are interpretable. What controlled means depends on the evaluation goal. A uniform sweep over reverberation time ensures no part of the RT range is underrepresented in the results. Alternatively, filtering for edge cases such as large source-receiver distances or high reverberation times is useful when conditions like these are underrepresented in standard benchmarks. Models often underperform on such cases in practice, so they deserve special attention in evaluation. Both strategies are about intentional selection rather than random sampling from whatever the collection contains.
The Treble SDK supports this through its IR collection sampling and filtering tools. Starting from an existing collection such as Treble10 or a collection generated with the ML training data generation workflow, you can enrich each IR with acoustic metadata and select a subset with specific coverage properties. The resulting evaluation set has known, controlled acoustic characteristics and can be used for evaluation at the IR level, at the device IR level, or at the scene level.
The curation tools are the same as those described in ML dataset curation and filtering. The difference is intent: here the goal is targeted selection for evaluation rather than broad coverage for training.
Evaluation workflow
The notebook below demonstrates the complete workflow using the Treble10 dataset. It covers two controlled selection strategies, audio processing with a speech enhancement model from HuggingFace, and PESQ-based evaluation across the evaluation set.
The model inference and metric computation steps use a SpeechBrain speech enhancement model and PESQ as a concrete example. However, the acoustic dataset construction steps apply to any audio ML task. Replace the model and metric with those relevant to your use case.
Webinar reference
The following recording from a Treble webinar demonstrates an earlier version of the ML evaluation workflow. It uses an older version of the SDK and an earlier version of the notebook above, so some API details and examples may differ from the current content.