Novel-View Acoustic Synthesis from 3D Reconstructed Rooms

• URL: http://arxiv.org/abs/2310.15130v2
• Date: Fri, 16 Aug 2024 01:35:52 GMT
• Title: Novel-View Acoustic Synthesis from 3D Reconstructed Rooms
• Authors: Byeongjoo Ahn, Karren Yang, Brian Hamilton, Jonathan Sheaffer, Anurag Ranjan, Miguel Sarabia, Oncel Tuzel, Jen-Hao Rick Chang,
• Abstract summary: We investigate the benefit of combining blind audio recordings with 3D scene information for novel-view acoustic synthesis. We identify the main challenges of novel-view acoustic synthesis as sound source localization, separation, and dereverberation. We show that incorporating room impulse responses (RIRs) derived from 3D reconstructed rooms enables the same network to jointly tackle these tasks.
• Score: 17.72902700567848
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate the benefit of combining blind audio recordings with 3D scene information for novel-view acoustic synthesis. Given audio recordings from 2-4 microphones and the 3D geometry and material of a scene containing multiple unknown sound sources, we estimate the sound anywhere in the scene. We identify the main challenges of novel-view acoustic synthesis as sound source localization, separation, and dereverberation. While naively training an end-to-end network fails to produce high-quality results, we show that incorporating room impulse responses (RIRs) derived from 3D reconstructed rooms enables the same network to jointly tackle these tasks. Our method outperforms existing methods designed for the individual tasks, demonstrating its effectiveness at utilizing 3D visual information. In a simulated study on the Matterport3D-NVAS dataset, our model achieves near-perfect accuracy on source localization, a PSNR of 26.44dB and a SDR of 14.23dB for source separation and dereverberation, resulting in a PSNR of 25.55 dB and a SDR of 14.20 dB on novel-view acoustic synthesis. We release our code and model on our project website at https://github.com/apple/ml-nvas3d. Please wear headphones when listening to the results.

Related papers

• 3D Audio-Visual Segmentation [44.61476023587931]
  Recognizing the sounding objects in scenes is a longstanding objective in embodied AI, with diverse applications in robotics and AR/VR/MR. We propose a new approach, EchoSegnet, characterized by integrating the ready-to-use knowledge from pretrained 2D audio-visual foundation models. Experiments demonstrate that EchoSegnet can effectively segment sounding objects in 3D space on our new benchmark, representing a significant advancement in the field of embodied AI.
  arXiv  Detail & Related papers  (2024-11-04T16:30:14Z)
• AV-GS: Learning Material and Geometry Aware Priors for Novel View Acoustic Synthesis [62.33446681243413]
  view acoustic synthesis aims to render audio at any target viewpoint, given a mono audio emitted by a sound source at a 3D scene. Existing methods have proposed NeRF-based implicit models to exploit visual cues as a condition for synthesizing audio. We propose a novel Audio-Visual Gaussian Splatting (AV-GS) model to characterize the entire scene environment. Experiments validate the superiority of our AV-GS over existing alternatives on the real-world RWAS and simulation-based SoundSpaces datasets.
  arXiv  Detail & Related papers  (2024-06-13T08:34:12Z)
• Real Acoustic Fields: An Audio-Visual Room Acoustics Dataset and Benchmark [65.79402756995084]
  Real Acoustic Fields (RAF) is a new dataset that captures real acoustic room data from multiple modalities. RAF is the first dataset to provide densely captured room acoustic data.
  arXiv  Detail & Related papers  (2024-03-27T17:59:56Z)
• Overview of the L3DAS23 Challenge on Audio-Visual Extended Reality [15.034352805342937]
  The primary goal of the L3DAS23 Signal Processing Grand Challenge at ICASSP 2023 is to promote and support collaborative research on machine learning for 3D audio signal processing. We provide a brand-new dataset, which maintains the same general characteristics of the L3DAS21 and L3DAS22 datasets. We propose updated baseline models for both tasks that can now support audio-image couples as input and a supporting API to replicate our results.
  arXiv  Detail & Related papers  (2024-02-14T15:34:28Z)
• Self-Supervised Visual Acoustic Matching [63.492168778869726]
  Acoustic matching aims to re-synthesize an audio clip to sound as if it were recorded in a target acoustic environment. We propose a self-supervised approach to visual acoustic matching where training samples include only the target scene image and audio. Our approach jointly learns to disentangle room acoustics and re-synthesize audio into the target environment, via a conditional GAN framework and a novel metric.
  arXiv  Detail & Related papers  (2023-07-27T17:59:59Z)
• AV-NeRF: Learning Neural Fields for Real-World Audio-Visual Scene Synthesis [61.07542274267568]
  We study a new task -- real-world audio-visual scene synthesis -- and a first-of-its-kind NeRF-based approach for multimodal learning. We propose an acoustic-aware audio generation module that integrates prior knowledge of audio propagation into NeRF. We present a coordinate transformation module that expresses a view direction relative to the sound source, enabling the model to learn sound source-centric acoustic fields.
  arXiv  Detail & Related papers  (2023-02-04T04:17:19Z)
• Listen2Scene: Interactive material-aware binaural sound propagation for reconstructed 3D scenes [69.03289331433874]
  We present an end-to-end audio rendering approach (Listen2Scene) for virtual reality (VR) and augmented reality (AR) applications. We propose a novel neural-network-based sound propagation method to generate acoustic effects for 3D models of real environments.
  arXiv  Detail & Related papers  (2023-02-02T04:09:23Z)
• AudioEar: Single-View Ear Reconstruction for Personalized Spatial Audio [44.460995595847606]
  We propose to achieve personalized spatial audio by reconstructing 3D human ears with single-view images. To fill the gap between the vision and acoustics community, we develop a pipeline to integrate the reconstructed ear mesh with an off-the-shelf 3D human body.
  arXiv  Detail & Related papers  (2023-01-30T02:15:50Z)
• Novel-View Acoustic Synthesis [140.1107768313269]
  We introduce the novel-view acoustic synthesis (NVAS) task. given the sight and sound observed at a source viewpoint, can we synthesize the sound of that scene from an unseen target viewpoint? We propose a neural rendering approach: Visually-Guided Acoustic Synthesis (ViGAS) network that learns to synthesize the sound of an arbitrary point in space.
  arXiv  Detail & Related papers  (2023-01-20T18:49:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.