10 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…
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…