3DAE: Binaural Quality Assessment for Audio Novel View Synthesis with Spatial Maps and Benchmark
3D audio and novel-view acoustic synthesis models are usually evaluated with global metrics.However, global metrics often hide where and why binaural prediction fails. We propose a full-reference diagnostic framework that uses time-frequency audio error maps for magnitude, ILD, IPD, temporal alignment, loudness, and high-frequency failures, forming a 3D Audio Error Map (3DAE Map) for visual inspection. We frame these diagnostics into a model-agnostic benchmark, Spatial Audio Error Bench (3DAE Bench), which takes arbitrary ground-truth and predicted binaural pairs and reports the prediction quality of audio novel-view synthesis models. Experiments on ViGAS outputs over Replay-NVAS and Soun...
| Authors | Jialu Xu, Yifan Zhou |
|---|---|
| Primary category | cs.SD |
| Published | 28 May 2026 |
| Last updated at source | 28 May 2026 |
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