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HiFi-HARP: A High-Fidelity 7th-Order Ambisonic Room Impulse Response Dataset

Shivam Saini, Jürgen Peissig

TL;DR

HiFi-HARP addresses the need for large-scale, high-fidelity HOA-RIR data by generating over 100k 7th-order Ambisonic RIRs in furnished indoor scenes using a hybrid wave-based and geometric simulation. The dataset merges low-frequency FDTD accuracy with high-frequency ray tracing, and encodes outputs into AmbiX ACN for direct auralization. It provides diverse room geometries, materials, and configurations, enabling benchmarks for FOA-to-HOA upsampling, DOA, and dereverberation, and demonstrates value through data augmentation for $T_{60}$ estimation and improved DOA generalization. This resource supports development of spatial audio algorithms for VR/AR and cross-modal acoustics, while acknowledging limitations like deterministic simulations and static scenes.

Abstract

We introduce HiFi-HARP, a large-scale dataset of 7th-order Higher-Order Ambisonic Room Impulse Responses (HOA-RIRs) consisting of more than 100,000 RIRs generated via a hybrid acoustic simulation in realistic indoor scenes. HiFi-HARP combines geometrically complex, furnished room models from the 3D-FRONT repository with a hybrid simulation pipeline: low-frequency wave-based simulation (finite-difference time-domain) up to 900 Hz is used, while high frequencies above 900 Hz are simulated using a ray-tracing approach. The combined raw RIRs are encoded into the spherical-harmonic domain (AmbiX ACN) for direct auralization. Our dataset extends prior work by providing 7th-order Ambisonic RIRs that combine wave-theoretic accuracy with realistic room content. We detail the generation pipeline (scene and material selection, array design, hybrid simulation, ambisonic encoding) and provide dataset statistics (room volumes, RT60 distributions, absorption properties). A comparison table highlights the novelty of HiFi-HARP relative to existing RIR collections. Finally, we outline potential benchmarks such as FOA-to-HOA upsampling, source localization, and dereverberation. We discuss machine learning use cases (spatial audio rendering, acoustic parameter estimation) and limitations (e.g., simulation approximations, static scenes). Overall, HiFi-HARP offers a rich resource for developing spatial audio and acoustics algorithms in complex environments.

HiFi-HARP: A High-Fidelity 7th-Order Ambisonic Room Impulse Response Dataset

TL;DR

HiFi-HARP addresses the need for large-scale, high-fidelity HOA-RIR data by generating over 100k 7th-order Ambisonic RIRs in furnished indoor scenes using a hybrid wave-based and geometric simulation. The dataset merges low-frequency FDTD accuracy with high-frequency ray tracing, and encodes outputs into AmbiX ACN for direct auralization. It provides diverse room geometries, materials, and configurations, enabling benchmarks for FOA-to-HOA upsampling, DOA, and dereverberation, and demonstrates value through data augmentation for estimation and improved DOA generalization. This resource supports development of spatial audio algorithms for VR/AR and cross-modal acoustics, while acknowledging limitations like deterministic simulations and static scenes.

Abstract

We introduce HiFi-HARP, a large-scale dataset of 7th-order Higher-Order Ambisonic Room Impulse Responses (HOA-RIRs) consisting of more than 100,000 RIRs generated via a hybrid acoustic simulation in realistic indoor scenes. HiFi-HARP combines geometrically complex, furnished room models from the 3D-FRONT repository with a hybrid simulation pipeline: low-frequency wave-based simulation (finite-difference time-domain) up to 900 Hz is used, while high frequencies above 900 Hz are simulated using a ray-tracing approach. The combined raw RIRs are encoded into the spherical-harmonic domain (AmbiX ACN) for direct auralization. Our dataset extends prior work by providing 7th-order Ambisonic RIRs that combine wave-theoretic accuracy with realistic room content. We detail the generation pipeline (scene and material selection, array design, hybrid simulation, ambisonic encoding) and provide dataset statistics (room volumes, RT60 distributions, absorption properties). A comparison table highlights the novelty of HiFi-HARP relative to existing RIR collections. Finally, we outline potential benchmarks such as FOA-to-HOA upsampling, source localization, and dereverberation. We discuss machine learning use cases (spatial audio rendering, acoustic parameter estimation) and limitations (e.g., simulation approximations, static scenes). Overall, HiFi-HARP offers a rich resource for developing spatial audio and acoustics algorithms in complex environments.
Paper Structure (11 sections, 4 figures, 3 tables)

This paper contains 11 sections, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology: Dataset Creation
  4. 3D-FRONT Scenes and Material Assignment
  5. Microphone Array Design
  6. Hybrid Acoustic Simulation
  7. Dataset Statistics and Comparison
  8. Room Acoustic Parameter Estimation
  9. Direction-of-Arrival Estimation
  10. Discussion
  11. Conclusion

Figures (4)

  • Figure 1: Example measurement configurations where green sphere is the spherical microphone array. The object colors do not accurately describe the material property and have been randomly assigned for visualisation purposes.
  • Figure 2: Quantitative Comparison of Treble Simulation and proposed approach. Broadband and octave-band T20 MAPE and EDF MSE, and DRR MSE of the simulated RIRs compared to the measured RIRs in room as provided in Genda. Note: The differences in results are likely due to the unavailability of precise material descriptions.
  • Figure 3: Comparison of parameters extracted from real measurement done using Eigenmike EM32 and our approach. Top row demonstrates the boxplot of errors for the respective measured position.
  • Figure 4: RT60 Distribution of the dataset