6 citations · 7 across the 13 of their papers we have counts for
6 papers · 1 filter
MQAScore: Fine-Grained Text-Music Alignment Evaluation via Large Audio Language Model
Wei-Jaw Lee, Chun-Ping Wang, Xuanjun Chen +1
Text-to-music alignment is commonly evaluated by the global similarity between text and audio embeddings, which collapses all described attributes into a single score and obscures…
What Does LALM Post-training Learn? Rethinking Audio Understanding and Reasoning Capabilities
Hua-Hsuan Chen, Xuanjun Chen, Wei-Chung Lu +5
Post-training often improves the performance of large audio-language models on audio understanding and reasoning benchmarks. However, these improvements may reflect adaptation to s…
Can LLM Agents Automate Reinforcement Learning for Text-to-Speech?
Xuanjun Chen, Zixiong Su, Hao Shi +4
Although reinforcement learning (RL) post-training repairs the localized segmental errors of zero-shot text-to-speech (TTS), arriving at a working recipe still relies on tedious ma…
MetaBench-Harness: Unlocking End-to-End Optimization of Benchmark Harnesses
Xuanjun Chen, Hua-Hsuan Chen, Wei-Chung Lu +3
Rapid progress in Large Language Models (LLMs) is saturating static benchmarks faster than they can be designed. While existing automated evolution frameworks attempt to generate h…
Mitigating Proxy-to-Wild Domain Gap in Deepfake Speech
Xuanjun Chen, Yun-Shing Wu, Wei-Chung Lu +4
Recent neural audio codec-based speech generation (CodecFake) produces highly realistic audio, posing a challenge to existing deepfake countermeasure models. While using codec resy…
Training-Efficient Text-to-Music Generation with State-Space Modeling
Wei-Jaw Lee, Fang-Chih Hsieh, Xuanjun Chen +2
Recent advances in text-to-music generation (TTM) have yielded high-quality results, but often at the cost of extensive compute and the use of large proprietary internal data. To i…