arXiv Machine Learning
ImpactMat: Continuous Material Estimation for Inverse Impact Sound Rendering
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arXiv AI
Oct 15
PhysicsSliders: Learning Physical Controls for Audio Foley Generation
arXiv:2610.18597v1 Announce Type: cross Abstract: Physical properties provide intuitive controls for sounds made by interacting objects. The challenge is to make a generative model respond predictabl...
arXiv Machine Learning
Jul 1
Step-by-Step Video-to-Audio Synthesis via Negative Audio Guidance
arXiv:2506. 20995v4 Announce Type: replace-cross Abstract: We propose a step-by-step video-to-audio (V2A) generation method that provides finer control over the generation process and more realistic audio synthesis.
arXiv Computer Vision
Sep 3
Video Object Segmentation-Aware Audio Generation
The paper introduces a new task called video object segmentation‑aware audio generation, which conditions sound synthesis on object‑level segmentation maps. It presents SAGANet, a multimodal generative model that uses visual segmentation masks, video, and textual cues to produce controllable audio for musical instruments, offering fine‑grained, visually localized control. The authors also release the Segmented Music Solos dataset of instrument performance videos with segmentation information to support this task and demonstrate that SAGANet outperforms current state‑of‑the‑art methods in controllable, high‑fidelity Foley synthesis.
arXiv AI
Jun 17
Explicit Context-Driven Neural Acoustic Modeling for High-Fidelity RIR Generation
arXiv:2509. 15210v2 Announce Type: replace-cross Abstract: Realistic sound simulation plays a critical role in many applications.
arXiv AI
Sep 7
SCAPES: Semantically Conditioned Autoregressive Prior for Environmental Sounds
SCAPES is a lightweight, resource‑efficient generative model that synthesizes high‑fidelity environmental sounds with high‑level semantic control. It operates on the continuous latent manifold of a neural audio codec, using a segmentation strategy and a Continuous Normalizing Flow to model latent trajectories. A 36‑million‑parameter instance can be trained on limited, uncurated data with a single consumer‑grade GPU, achieving convergence in roughly twice the source audio duration and enabling smooth semantic interpolation.
arXiv AI
Sep 24
SsgCaps: A controlled dataset for the evaluation of sound scene generation algorithms
SsgCaps is a publicly available dataset of human-engineered sound scenes, each paired with a precisely structured prompt that guides the sampling process. The prompts are drawn from a predefined action-based typology, enabling extensive yet plausible sampling. A comparative quantitative analysis shows only small differences between the open and private versions, supporting the recommendation of the open version for benchmarking sound scene generation algorithms.