This work introduces a feature extracted from stereophonic/binaural audio signals aiming to represent a measure of perceived quality degradation in processed spatial auditory scenes. The feature extraction technique is based on a simplified stereo signal model considering auditory events positioned towards a given direction in the stereo field using amplitude panning (AP) techniques. We decompose the stereo signal into a set of directional signals for given AP values in the Short-Time Fourier Transform domain and calculate their overall loudness to form a directional loudness representation or maps. Then, we compare directional loudness maps of a reference signal and a deteriorated version to derive a distortion measure aiming to describe the associated perceived degradation scores reported in listening tests. The measure is then tested on an extensive listening test database with stereo signals processed by state-of-the-art perceptual audio codecs using non waveform-preserving techniques such as bandwidth extension and joint stereo coding, known for presenting a challenge to existing quality predictors. Results suggest that the derived distortion measure can be incorporated as an extension to existing automated perceptual quality assessment algorithms for improving prediction on spatially coded audio signals.
Tri-Ergon: Fine-grained Video-to-Audio Generation with Multi-modal Conditions and LUFS Control
vivo Mobile Communication Co., Ltd（维沃移动通信有限公司）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Northwestern Polytechnical University（西北工业大学）；OpenNLPLab
Can we still use PEAQ? A Performance Analysis of the ITU Standard for the Objective Assessment of Perceived Audio Quality
International Audio Laboratories Erlangen（埃尔朗根国际音频实验室）；Friedrich-Alexander Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Fraunhofer IIS（弗劳恩霍夫集成电路研究所）
SALM: Spatial Audio Language Model with Structured Embeddings for Understanding and Editing
Institute of Acoustics, Chinese Academy of Sciences, China（中国科学院声学研究所）；MiLM Plus, Xiaomi Inc., China（小米公司）；Xi’an Jiaotong Liverpool University, China（西安交通大学利物浦大学）；University of Chinese Academy of Sciences, China（中国科学院大学）
Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey, UK（视觉、语音和信号处理中心（CVSSP），英国萨里大学）；Tencent AI Lab, Bellevue, WA, USA（腾讯AI实验室，美国西雅图）；Department of Computer Science and Technology, University of Science and Technology Beijing, China（北京科技大学计算机科学与技术系）；The Chinese University of Hong Kong (CUHK)（香港中文大学）
OmniAudio: Generating Spatial Audio from 360-Degree Video
Hong Kong University of Science and Technology（香港科技大学）；Tongyi Lab, Alibaba Group（阿里巴巴集团通义实验室）；Zhejiang University（浙江大学）；FAIR, Meta（Meta FAIR）；Nanyang Technological University（南洋理工大学）
Speech Audio Synthesis from Tagged MRI and Non-Negative Matrix Factorization via Plastic Transformer
Massachusetts General Hospital（马萨诸塞州总医院）；Harvard Medical School（哈佛医学院）；University of Maryland（马里兰大学）；University of British Columbia（不列颠哥伦比亚大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Columbia University（哥伦比亚大学）；Institute for Clarity in Documentation（清晰文档研究所）；Inria Paris-Rocquencourt（Inria巴黎- Rocquencourt研究所）；Rajiv Gandhi University（拉贾·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒姆研究实验室）
