M2PAIR: A High-Quality Acoustic Impulse Response Computation Model

Zhiyu Li, Xinpei Zhao, Jing Wang, Xinyuan Qian, Xiang Xie

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Acoustic Impulse Response (AIR) provides crucial spatial information about the environment, significantly enhancing audio immersion. However, achieving high perceptual quality while computing AIR in real-time for interactive audio-video media (IAVM) presents a challenging problem. This study proposes the Mesh to Parametric AIR (M2PAIR), a method for computing AIR designed for IAVM. M2PAIR integrates neural networks with psychoacoustics. It takes the 3D scene mesh, the listener positions, and the sound source positions as inputs, utilizes perceptual parameters as intermediaries, and computes the desired high-quality AIR signal based on these parameters. Experimental results demonstrate that M2PAIR improves the perceptual quality of AIR output compared to existing methods while reducing the model complexity. Additionally, it meets the requirements of IAVM, including real-time computation, high sampling rates, and flexible duration for the output AIR.

Original languageEnglish
Title of host publication2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 - Proceedings
EditorsBhaskar D Rao, Isabel Trancoso, Gaurav Sharma, Neelesh B. Mehta
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350368741
DOIs
Publication statusPublished - 2025
Externally publishedYes
Event2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 - Hyderabad, India
Duration: 6 Apr 202511 Apr 2025

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025
Country/TerritoryIndia
CityHyderabad
Period6/04/2511/04/25

Keywords

  • Acoustic Impulse Response
  • Auralization
  • Deep Learning
  • Interactive Media
  • Psychoacoustics

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