HARP: A Large-Scale Higher-Order Ambisonic Room Impulse Response Dataset
This dataset provides a vital resource for researchers in spatial audio, particularly for machine learning applications in room acoustics modeling and sound field synthesis.
The authors introduced HARP, a large-scale dataset of 7th-order Ambisonic Room Impulse Responses (HOA-RIRs) generated using the Image Source Method, featuring 64-microphone configurations and diverse room parameters to enable precise spatial audio reproduction for immersive applications.
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.