4 citations · 4 across the 18 of their papers we have counts for
12 papers · 1 filter
VoiceTTA: Enhancing Zero-Shot Text-to-Speech via Reinforcement Learning-Based Test-Time Adaptation
Tianxin Xie, Chenxing Li, Dong Yu +1
Recently, zero-shot text-to-speech (TTS) has enabled high-fidelity and expressive speech synthesis, but it often fails to imitate unseen speaking styles from uncommon scenarios (e.…
Audio-DeepThinker: Progressive Reasoning-Aware Reinforcement Learning for High-Quality Chain-of-Thought Emergence in Audio Language Models
Xiang He, Chenxing Li, Jinting Wang +5
Large Audio-Language Models (LALMs) have made significant progress in audio understanding, yet they primarily operate as perception-and-answer systems without explicit reasoning pr…
SemanticVocoder: Bridging Audio Generation and Audio Understanding via Semantic Latents
Zeyu Xie, Chenxing Li, Qiao Jin +6
Recent audio generation models typically rely on Variational Autoencoders (VAEs) and perform generation within the VAE latent space. Although VAEs excel at compression and reconstr…
PhyAVBench: A Challenging Audio Physics-Sensitivity Benchmark for Physically Grounded Text-to-Audio-Video Generation
Tianxin Xie, Wentao Lei, Kai Jiang +27
Text-to-audio-video (T2AV) generation is central to applications such as filmmaking and world modeling. However, current models often fail to produce physically plausible sounds. P…
AudioRAG+: Feedback-driven Retrieval-augmented Audio Generation with Large Audio Language Models
Junqi Zhao, Chenxing Li, Jinzheng Zhao +4
We propose a general feedback-driven retrieval-augmented generation (RAG) approach that leverages Large Audio Language Models (LALMs) to address the missing or imperfect synthesis…
AudioGenie-Reasoner: A Training-Free Multi-Agent Framework for Coarse-to-Fine Audio Deep Reasoning
Yan Rong, Chenxing Li, Dong Yu +1
Audio deep reasoning is a challenging task that requires expert-level perception, multi-step logical inference, and the integration of contextual knowledge. However, existing model…
GACA-DiT: Diffusion-based Dance-to-Music Generation with Genre-Adaptive Rhythm and Context-Aware Alignment
Jinting Wang, Chenxing Li, Li Liu
Dance-to-music (D2M) generation aims to automatically compose music that is rhythmically and temporally aligned with dance movements. Existing methods typically rely on coarse rhyt…
VCB Bench: An Evaluation Benchmark for Audio-Grounded Large Language Model Conversational Agents
Jiliang Hu, Wenfu Wang, Zuchao Li +6
Recent advances in large audio language models (LALMs) have greatly enhanced multimodal conversational systems. However, existing benchmarks remain limited -- they are mainly Engli…
Audio-Thinker: Guiding Audio Language Model When and How to Think via Reinforcement Learning
Shu Wu, Chenxing Li, Wenfu Wang +4
Recent advancements in large language models, multimodal large language models, and large audio language models (LALMs) have significantly improved their reasoning capabilities thr…
EmoSteer-TTS: Fine-Grained and Training-Free Emotion-Controllable Text-to-Speech via Activation Steering
Tianxin Xie, Shan Yang, Chenxing Li +2
Text-to-speech (TTS) has shown great progress in recent years. However, most existing TTS systems offer only coarse and rigid emotion control, typically via discrete emotion labels…
Dopamine Audiobook: A Training-free MLLM Agent for Emotional and Immersive Audiobook Generation
Yan Rong, Shan Yang, Chenxing Li +2
Audiobook generation aims to create rich, immersive listening experiences from multimodal inputs, but current approaches face three critical challenges: (1) the lack of synergistic…
SRC-gAudio: Sampling-Rate-Controlled Audio Generation
Chenxing Li, Manjie Xu, Dong Yu
We introduce SRC-gAudio, a novel audio generation model designed to facilitate text-to-audio generation across a wide range of sampling rates within a single model architecture. SR…