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

TL;DR

This paper introduces HARP, a comprehensive dataset of 7th-order Ambisonic Room Impulse Responses generated via simulation, enabling advanced spatial audio research with high realism and diverse room configurations.

Contribution

The creation of a large-scale, high-order Ambisonic RIR dataset using a novel microphone configuration and detailed simulation setup for improved spatial audio applications.

Findings

Provides a wide variety of room configurations and acoustic conditions.

Enables precise sound field capture directly in the Spherical Harmonics domain.

Supports research in source localization, reverberation, and immersive audio.

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.