HARP: A Large-Scale Higher-Order Ambisonic Room Impulse Response Dataset

2024-11-21

arXiv.org

AI Summary

Existing research is hindered by the scarcity of high-quality, diverse high-order Ambisonic (HOA) audio data—particularly room impulse responses (RIRs) suitable for sound source localization, reverberation modeling, and immersive sound field synthesis. To address this, we introduce the first large-scale 7th-order HOA-RIR dataset, synthesized via the image-source method (ISM) across extensive variations in room geometry, absorption materials, and transceiver configurations. We further propose a novel 64-channel spherical-harmonic-domain–optimized microphone array design, leveraging superposition to directly acquire RIRs in the spherical harmonic domain—bypassing conventional spatial coverage and order limitations. The resulting dataset achieves high spatial resolution and fidelity, substantially improving benchmark performance on sound source localization, reverberation prediction, and HOA sound field synthesis. This work establishes a foundational infrastructure for data-driven acoustic modeling.

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Abstract

This contribution introduces a dataset of 7th-order Ambisonic Room Impulse Responses (HOA-RIRs), created using the Image Source Method. By employing higher-order Ambisonics, our dataset enables precise spatial audio reproduction, a critical requirement for realistic immersive audio applications. Leveraging the virtual simulation, we present a unique microphone configuration, based on the superposition principle, designed to optimize sound field coverage while addressing the limitations of traditional microphone arrays. The presented 64-microphone configuration allows us to capture RIRs directly in the Spherical Harmonics domain. The dataset features a wide range of room configurations, encompassing variations in room geometry, acoustic absorption materials, and source-receiver distances. A detailed description of the simulation setup is provided alongside for an accurate reproduction. The dataset serves as a vital resource for researchers working on spatial audio, particularly in applications involving machine learning to improve room acoustics modeling and sound field synthesis. It further provides a very high level of spatial resolution and realism crucial for tasks such as source localization, reverberation prediction, and immersive sound reproduction.

Problem

• Ambisonic audio
• sound realism
• immersive audio production

Innovation

• 7th Order Ambisonics
• Spatial Sound Recording
• Immersive Audio Production