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Hearing Anything Anywhere
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Proposes DIFFRIR, a differentiable room impulse response renderer with parametric source directivity and surface reflectivity models, fit from ~12 sparse RIR recordings plus a planar room geometry to render RIRs and music at unseen listener locations.
Problem
Reconstructing the spatial acoustic characteristics of a real room (RIRs and music at unseen listener positions) from only a sparse set of about 12 RIR measurements and a rough planar room geometry.
Approach
A fully differentiable image-source-based path tracing renderer with interpretable learned source directivity (spherical heatmaps), source impulse response, and per-surface frequency-dependent reflectivity, plus a learned spatially uniform residual for late reverberation, fit by analysis-by-synthesis against measured RIRs.
Key results
- On monaural RIR prediction at unseen locations, DIFFRIR achieves the lowest Mag and ENV errors across all four rooms, e.g., Classroom RIR Mag 5.22 vs. 5.99 for NN and 6.36 for NAF.
- Ablating the residual component raises Complex Room RIR Mag error from 9.13 to 15.49.
- Trained on 6 points, DIFFRIR reportedly outperforms all baselines trained on 100 points.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use DIFFRIR rather than nearest-neighbor interpolation | Monaural RIR and music rendering · DIFFRIR Dataset (four rooms) | Mag: Classroom RIR: 5.99 -> 5.22; Dampened Room RIR: 1.36 -> 1.21; Hallway RIR: 10.14 -> 9.13; Complex Room RIR: 5.52 -> 4.86 | Helped |
| Add a learned source directivity pattern (ablation) | DIFFRIR · DIFFRIR Dataset, four rooms | RIR Mag: Classroom: 5.22 -> 5.47; Dampened Room: 1.21 -> 1.64; Hallway: 9.13 -> 9.98; Complex Room: 4.86 -> 5.13 | Helped |
| Add a learned source impulse response (ablation) | DIFFRIR · DIFFRIR Dataset, four rooms | RIR Mag: Classroom: 5.22 -> 5.39; Dampened Room: 1.21 -> 1.36; Hallway: 9.13 -> 9.38; Complex Room: 4.86 -> 5.07 | Helped |
| Add the residual component (ablation) | DIFFRIR · DIFFRIR Dataset, four rooms | RIR Mag: Classroom: 5.22 -> 6.90; Dampened Room: 1.21 -> 1.37; Hallway: 9.13 -> 15.49; Complex Room: 4.86 -> 6.24 | Helped |
Surprising findings
- Naive DeepIR performs worse than simple nearest-neighbor and linear interpolation baselines on several rooms, e.g., Complex Room RIR Mag 15.71 vs. 10.14 for NN.
Limitations
- Assumes a stationary source with unknown position/orientation and a planar room geometry approximation.
- Does not explicitly model diffuse reflection, diffraction, transmission, or refraction; these are approximated by a spatially uniform residual.
Experimental setup
Datasets: DIFFRIR Dataset Free Music Archive
Benchmarks: DIFFRIR Dataset
Metrics: Multiscale Log-Spectral L1 (Mag) Envelope Distance (ENV)
Baselines: Nearest neighbor Linear interpolation DeepIR NAF INRAS
Builds on: Neural Acoustic Fields Deep Impulse Response Image-source method Differentiable audio rendering
Closest papers by meaning
- Hearing Anywhere in Any Environment (CVPR 2025 · similarity 0.92)
- Acoustic Volume Rendering for Neural Impulse Response Fields (NeurIPS 2024 · similarity 0.88)
- Resounding Acoustic Fields with Reciprocity (NeurIPS 2025 · similarity 0.88)
- Audible World Models: Spatially Aware Sound Generation for 3D Worlds (NeurIPS 2026 · similarity 0.87)
- SoundVista: Novel-View Ambient Sound Synthesis via Visual-Acoustic Binding (CVPR 2025 · similarity 0.87)
- NeRAF: 3D Scene Infused Neural Radiance and Acoustic Fields (ICLR 2025 · similarity 0.87)
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