arXiv Machine Learning

AudioWorldSim: Realistic Binaural Audio Datasets For World Models

AudioWorldSim is an open‑source platform that generates realistic binaural audio datasets for training and evaluating audio‑based machine learning models, especially world models. It extends Meta’s SoundSpaces 2.0 by automating random agent navigation and correcting continuous sound composition. The project is publicly available on GitHub to support reproducibility in research.

arXiv Machine Learning
Sep 10

BinauralVAE: Spatial Audio Reconstruction For World Models

BinauralVAE is an open‑source pipeline that reconstructs spatial audio using various Variational Autoencoder architectures, including complex‑valued variants, to learn latent representations of binaural signals. The project builds on realistic acoustic data from a simulated robot navigating an environment, providing a foundation for audio‑centric world models. It aims to map the causal link between navigational actions and their acoustic outcomes, positioning sound as a complementary modality for spatial awareness.

By Luis Vitor Zerkowski, Luiz Velho
arXiv Computation and Language
Sep 10

NOPE-HYPE: A Structured Simulation Workflow for Robust Speech-to-Text Across Diverse Acoustic Environments

NOPE-HYPE is a structured training workflow that integrates a controllable environment simulator, a coverage‑optimal reduction of Power Spectral Density templates, and a concise hyperparameter search over simulator settings. The authors demonstrate that noise generated by the simulator can match the performance of balanced real‑noise training for Whisper and SeamlessM4T models. They also provide principled environment prototype sets and practical default simulator configurations derived from a 27‑run hyperparameter sweep.

By Niramay M. Patel, Bibek Behera, Raksha Sharma
arXiv AI
Sep 1

PhysWave: Physics-Guided Latent Diffusion Models for Controllable Spatial Audio Generation

PhysWave is a physics-guided latent diffusion model designed for controllable text-to-First-Order Ambisonics (FOA) audio generation. It integrates natural-language and trajectory control via a shared waypoint-caption representation and incorporates two differentiable acoustic priors—spherical-harmonic direction consistency and inverse-square distance consistency—into diffusion training. The authors also introduce a 300K-clip FOA dataset and demonstrate that these priors improve spatial consistency while preserving audio quality, with potential use as inference-time guidance for training-free refinement.

By Lingfeng Yao, Chenpei Huang, Xingke Yang, Ziye Geng, Changqing Luo, Hao Wang, Jiang Liu, Miao Pan
arXiv AI
Jun 9

Audio-FLAN: An Instruction-Following Dataset for Unified Audio Understanding and Generation of Speech, Music, and Sound

arXiv:2502. 16584v2 Announce Type: replace-cross Abstract: Recent advancements in audio tokenization have significantly enhanced the integration of audio capabilities into large language models (LLMs).

By Liumeng Xue, Ziya Zhou, Jiahao Pan, Zixuan Li, Shuai Fan, Yinghao Ma, Sitong Cheng, Dongchao Yang, Haohan Guo, Yujia Xiao, Xinsheng Wang, Zixuan Shen, Chuanbo Zhu, Xinshen Zhang, Tianchi Liu, Ruibin Yuan, Zeyue Tian, Haohe Liu, Xingjian Du, Emmanouil Benetos, Ge Zhang, Yike Guo, Wei Xue