8 papers
SALMONN-2: Advancing General-Purpose Hearing Abilities with Self-Supervised Representations
Xiaoyu Yang, Xuenan Xu, Wenyi Yu +10
Recent audio large language models (ALLMs) are typically built upon audio encoders trained with large amounts of supervised data. Since self-supervised learning (SSL) audio encoder…
End-to-end Listen, Look, Speak and Act
Siyin Wang, Wenyi Yu, Xianzhao Chen +4
Human interaction is inherently multimodal and full-duplex: we listen while watching, speak while acting, and fluidly adapt to turn-taking and interruptions. Realizing these capabi…
Towards General Auditory Intelligence: Large Multimodal Models for Machine Listening and Speaking
Siyin Wang, Zengrui Jin, Changli Tang +26
In the era of large language models (LLMs) and artificial general intelligence (AGI), computer audition must evolve beyond traditional paradigms to fully leverage the capabilities…
Extract and Diffuse: Latent Integration for Improved Diffusion-based Speech and Vocal Enhancement
Yudong Yang, Zhan Liu, Wenyi Yu +3
Diffusion-based generative models have recently achieved remarkable results in speech and vocal enhancement due to their ability to model complex speech data distributions. While t…
MMAU-Pro: A Challenging and Comprehensive Benchmark for Holistic Evaluation of Audio General Intelligence
Sonal Kumar, Å imon SedláÄek, Vaibhavi Lokegaonkar +31
Audio comprehension-including speech, non-speech sounds, and music-is essential for achieving human-level intelligence. Consequently, AI agents must demonstrate holistic audio unde…
QualiSpeech: A Speech Quality Assessment Dataset with Natural Language Reasoning and Descriptions
Siyin Wang, Wenyi Yu, Xianzhao Chen +7
This paper explores a novel perspective to speech quality assessment by leveraging natural language descriptions, offering richer, more nuanced insights than traditional numerical…