BAT: Learning to Reason about Spatial Sounds with Large Language Models

• URL: http://arxiv.org/abs/2402.01591v2
• Date: Sat, 25 May 2024 17:05:11 GMT
• Title: BAT: Learning to Reason about Spatial Sounds with Large Language Models
• Authors: Zhisheng Zheng, Puyuan Peng, Ziyang Ma, Xie Chen, Eunsol Choi, David Harwath,
• Abstract summary: We present BAT, which combines the sound perception ability of a spatial scene analysis model with the natural language reasoning capabilities of a large language model (LLM) Our experiments demonstrate BAT's superior performance on both spatial sound perception and reasoning.
• Score: 45.757161909533714
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spatial sound reasoning is a fundamental human skill, enabling us to navigate and interpret our surroundings based on sound. In this paper we present BAT, which combines the spatial sound perception ability of a binaural acoustic scene analysis model with the natural language reasoning capabilities of a large language model (LLM) to replicate this innate ability. To address the lack of existing datasets of in-the-wild spatial sounds, we synthesized a binaural audio dataset using AudioSet and SoundSpaces 2.0. Next, we developed SpatialSoundQA, a spatial sound-based question-answering dataset, offering a range of QA tasks that train BAT in various aspects of spatial sound perception and reasoning. The acoustic front end encoder of BAT is a novel spatial audio encoder named Spatial Audio Spectrogram Transformer, or Spatial-AST, which by itself achieves strong performance across sound event detection, spatial localization, and distance estimation. By integrating Spatial-AST with LLaMA-2 7B model, BAT transcends standard Sound Event Localization and Detection (SELD) tasks, enabling the model to reason about the relationships between the sounds in its environment. Our experiments demonstrate BAT's superior performance on both spatial sound perception and reasoning, showcasing the immense potential of LLMs in navigating and interpreting complex spatial audio environments.

Related papers

• ImmerseDiffusion: A Generative Spatial Audio Latent Diffusion Model [2.2927722373373247]
  We introduce ImmerseDiffusion, an end-to-end generative audio model that produces 3D immersive soundscapes conditioned on the spatial, temporal, and environmental conditions of sound objects.
  arXiv  Detail & Related papers  (2024-10-19T02:28:53Z)
• Both Ears Wide Open: Towards Language-Driven Spatial Audio Generation [32.24603883810094]
  Controlling stereo audio with spatial contexts remains challenging due to high data costs and unstable generative models. We first construct a large-scale, simulation-based, and GPT-assisted dataset, BEWO-1M, with abundant soundscapes and descriptions even including moving and multiple sources. By leveraging spatial guidance, our unified model achieves the objective of generating immersive and controllable spatial audio from text and image.
  arXiv  Detail & Related papers  (2024-10-14T16:18:29Z)
• 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)
• Audio-Visual Spatial Integration and Recursive Attention for Robust Sound Source Localization [13.278494654137138]
  Humans utilize both audio and visual modalities as spatial cues to locate sound sources. We propose an audio-visual spatial integration network that integrates spatial cues from both modalities. Our method can perform more robust sound source localization.
  arXiv  Detail & Related papers  (2023-08-11T11:57:58Z)
• 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)
• SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning [127.1119359047849]
  We introduce SoundSpaces 2.0, a platform for on-the-fly geometry-based audio rendering for 3D environments. It generates highly realistic acoustics for arbitrary sounds captured from arbitrary microphone locations. SoundSpaces 2.0 is publicly available to facilitate wider research for perceptual systems that can both see and hear.
  arXiv  Detail & Related papers  (2022-06-16T17:17:44Z)
• Geometry-Aware Multi-Task Learning for Binaural Audio Generation from Video [94.42811508809994]
  We propose an audio spatialization method that draws on visual information in videos to convert their monaural (single-channel) audio to audio. Whereas existing approaches leverage visual features extracted directly from video frames, our approach explicitly disentangles the geometric cues present in the visual stream to guide the learning process.
  arXiv  Detail & Related papers  (2021-11-21T19:26:45Z)
• Visually Informed Binaural Audio Generation without Binaural Audios [130.80178993441413]
  We propose PseudoBinaural, an effective pipeline that is free of recordings. We leverage spherical harmonic decomposition and head-related impulse response (HRIR) to identify the relationship between spatial locations and received audios. Our-recording-free pipeline shows great stability in cross-dataset evaluation and achieves comparable performance under subjective preference.
  arXiv  Detail & Related papers  (2021-04-13T13:07:33Z)

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.