activity
20232026
most citedQwen2-Audio Technical Report

7 citations · 7 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG2026

FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion

Tao Fan, Guoqiang Ma, Yuanfeng Song +3

Federated fine-tuning of Large Language Models (LLMs) is obstructed by a trilemma of challenges: protecting LLMs intellectual property (IP), ensuring client privacy, and mitigating…

cs.SD2025

Stepback: Enhanced Disentanglement for Voice Conversion via Multi-Task Learning

Qian Yang, Calbert Graham

Voice conversion (VC) modifies voice characteristics while preserving linguistic content. This paper presents the Stepback network, a novel model for converting speaker identity us…

eess.AS20247 cited

Qwen2-Audio Technical Report

Yunfei Chu, Jin Xu, Qian Yang +9

We introduce the latest progress of Qwen-Audio, a large-scale audio-language model called Qwen2-Audio, which is capable of accepting various audio signal inputs and performing audi…

eess.AS2024

AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension

Qian Yang, Jin Xu, Wenrui Liu +8

Recently, instruction-following audio-language models have received broad attention for human-audio interaction. However, the absence of benchmarks capable of evaluating audio-cent…

eess.AS2023

Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models

Yunfei Chu, Jin Xu, Xiaohuan Zhou +5

Recently, instruction-following audio-language models have received broad attention for audio interaction with humans. However, the absence of pre-trained audio models capable of h…