While audio quality is a key performance metric for various audio processing tasks, including generative modeling, its objective measurement remains a challenge. Audio-Language Models (ALMs) are pre-trained on audio-text pairs that may contain information about audio quality, the presence of artifacts, or noise. Given an audio input and a text prompt related to quality, an ALM can be used to calculate a similarity score between the two. Here, we exploit this capability and introduce PAM, a no-reference metric for assessing audio quality for different audio processing tasks. Contrary to other "reference-free" metrics, PAM does not require computing embeddings on a reference dataset nor training a task-specific model on a costly set of human listening scores. We extensively evaluate the reliability of PAM against established metrics and human listening scores on four tasks: text-to-audio (TTA), text-to-music generation (TTM), text-to-speech (TTS), and deep noise suppression (DNS). We perform multiple ablation studies with controlled distortions, in-the-wild setups, and prompt choices. Our evaluation shows that PAM correlates well with existing metrics and human listening scores. These results demonstrate the potential of ALMs for computing a general-purpose audio quality metric.
Enhancing Speech Large Language Models with Prompt-Aware Mixture of Audio Encoders
School of Computer Science and Engineering, Northeastern University（东北大学计算机科学与工程学院）；Huawei Translation Services Center（华为翻译服务中心）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；College of Computer Science and Technology, Harbin Engineering University（哈尔滨工程大学计算机科学与技术学院）；NiuTrans Research（NiuTrans研究院）
Beyond Accuracy: ARIA-Rubrics for Evaluating Audio Reasoning in Large Audio Language Models
Imperial College London（帝国理工学院）；Technische Universität München（慕尼黑工业大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Shanghai Jiao Tong University（上海交通大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Textual Acoustic Grounding for Generalizable LLM-Based Deepfake Voice Detection
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；National Institute of Informatics（情报信息学研究所）；Technical University of Berlin（柏林工业大学）
Selective Listening: Mechanism-Guided Control of Audio Influence in Large Audio-Language Models
College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机学院）；State Key Laboratory of Complex & Critical Software Environment（复杂与关键软件环境国家重点实验室）
Unifying Speech Editing Detection and Content Localization via Prior-Enhanced Audio LLMs
Key Laboratory of Aerospace Information Security（航空信息安全与可信计算重点实验室）；School of Cyber Science and Engineering（网络安全工程学院）；Wuhan University（武汉大学）；Independent Researcher（独立研究员）；School of Computer Science and Technology（计算机科学与技术学院）；Anhui University（安徽大学）；Communication University of China（中国通信大学）；Beihang University（北京航空航天大学）
Leveraging tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacoustics
University College London（伦敦大学学院）；Google DeepMind（谷歌DeepMind）；Zoological Society of London（伦敦动物学会）；Conservation Metrics Inc.（保护指标公司）；University of New Hampshire（新罕布什尔大学）；K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University（康奈尔大学鸟类学实验室K. Lisa Yang保护生物声学中心）；Department of Public and Ecosystem Health, Cornell University（康奈尔大学公共与生态系统健康系）；Sound Ocean Science（海洋声学科学）；University of Exeter（埃克塞特大学）；University of Bristol（布里斯托大学）；IPB University（IPB大学）；Google Research（谷歌研究院）
ALLM4ADD: Unlocking the Capabilities of Audio Large Language Models for Audio Deepfake Detection
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Department of Automation, Tsinghua University（清华大学自动化系）；Taizhou University（台州大学）；Anhui University（安徽大学）；Beijing National Research Center for Information Science and Technology,Tsinghua University（北京信息科学与技术国家研究中心，清华大学）
A Survey of Audio Reasoning in Multimodal Foundation Models
The Chinese University of Hong Kong（香港中文大学）；National Taiwan University（国立台湾大学）；The University of Hong Kong（香港大学）；The Hong Kong University of Science and Technology（香港科技大学）
Fine-Tuning Large Audio-Language Models with LoRA for Precise Temporal Localization of Prolonged Exposure Therapy Elements
1College of Information Sciences \& Technology, The Pennsylvania State University, USA 2Department of Psychiatry \& Behavioral Sciences, Emory University, USA 3School of Interactive Computing, Georgia Institute of Technology, USA 4School of Psychology, Georgia Institute of Technology, USA
