8 citations · 9 across the 14 of their papers we have counts for
16 papers
High-Fidelity Song Generation with Joint Discrete and Continuous Modeling
Zheqi Dai, Zhiyue Wu, Ruibin Yuan +5
Generating a full song from lyrics and a style description requires long-range musical planning without sacrificing vocal and instrumental detail. Low-rate discrete semantic tokens…
HMU-Bench: Hierarchical Music Understanding Benchmark for Omni Models
Zheqi Dai, Boyang Wang, Zhiyue Wu +6
Music-understanding benchmarks should identify both the evidence available to a model and the operations a correct answer requires. We introduce HMU-Bench, a bilingual benchmark wi…
Summary of DCASE 2026 Task 5: Audio-Dependent Question Answering
Haolin He, Renhe Sun, Zheqi Dai +16
DCASE~2026 Task~5 introduces Audio-Dependent Question Answering (ADQA), which tests whether large audio-language models answer from the audio rather than from textual priors. An Au…
One-Step Token-to-Waveform Generation with MeanFlow in Latent Space
Zheqi Dai, Guangyan Zhang, Zhen Ye +5
Neural audio codecs are central to modern LLM-based Text-to-Speech (TTS) and multimodal systems. As low-bitrate semantic codecs gain prominence, the Token-to-Waveform (Token2Wav) d…
Which Speech Representation Better Matches Text-Native Reasoning? A Study of Speech-Text Alignment on Frame Rate and Representation
Zhen Ye, Xu Tan, Yiming Li +10
Spoken dialogue models typically start from text LLM backbones, yet reasoning often degrades when conditioning on speech instead of text. We attribute part of this modality gap to…
FineLAP: Taming Heterogeneous Supervision for Fine-grained Language-Audio Pretraining
Xiquan Li, Xuenan Xu, Ziyang Ma +4
Contrastively pretrained audio-language models (e.g., CLAP) excel at clip-level understanding but struggle with frame-level tasks. Existing extensions fail to exploit the varying g…
ARCHI-TTS: A flow-matching-based Text-to-Speech Model with Self-supervised Semantic Aligner and Accelerated Inference
Chunyat Wu, Jiajun Deng, Zhengxi Liu +3
Although diffusion-based, non-autoregressive text-to-speech (TTS) systems have demonstrated impressive zero-shot synthesis capabilities, their efficacy is still hindered by two key…
SemanticAudio: Audio Generation and Editing in Semantic Space
Zheqi Dai, Guangyan Zhang, Haolin He +5
In recent years, Text-to-Audio Generation has achieved remarkable progress, offering sound creators powerful tools to transform textual inspirations into vivid audio. However, exis…
PodEval: A Multimodal Evaluation Framework for Podcast Audio Generation
Yujia Xiao, Liumeng Xue, Lei He +8
Recently, an increasing number of multimodal (text and audio) benchmarks have emerged, primarily focusing on evaluating models' understanding capability. However, exploration into…
Measuring Audio's Impact on Correctness: Audio-Contribution-Aware Post-Training of Large Audio Language Models
Haolin He, Xingjian Du, Renhe Sun +16
Large Audio Language Models (LALMs) represent an important frontier in multimodal AI, addressing diverse audio tasks. Recently, post-training of LALMs has received increasing atten…
Inference-time Scaling for Diffusion-based Audio Super-resolution
Yizhu Jin, Zhen Ye, Zeyue Tian +4
Diffusion models have demonstrated remarkable success in generative tasks, including audio super-resolution (SR). In many applications like movie post-production and album masterin…
Llasa: Scaling Train-Time and Inference-Time Compute for Llama-based Speech Synthesis
Zhen Ye, Xinfa Zhu, Chi-Min Chan +17
Recent advances in text-based large language models (LLMs), particularly in the GPT series and the o1 model, have demonstrated the effectiveness of scaling both training-time and i…
Foundation Models for Music: A Survey
Yinghao Ma, Anders Øland, Anton Ragni +39
In recent years, foundation models (FMs) such as large language models (LLMs) and latent diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This com…
Codec Does Matter: Exploring the Semantic Shortcoming of Codec for Audio Language Model
Zhen Ye, Peiwen Sun, Jiahe Lei +9
Recent advancements in audio generation have been significantly propelled by the capabilities of Large Language Models (LLMs). The existing research on audio LLM has primarily focu…
MusicScore: A Dataset for Music Score Modeling and Generation
Yuheng Lin, Zheqi Dai, Qiuqiang Kong
Music scores are written representations of music and contain rich information about musical components. The visual information on music scores includes notes, rests, staff lines,…
Joint Music and Language Attention Models for Zero-shot Music Tagging
Xingjian Du, Zhesong Yu, Jiaju Lin +2
Music tagging is a task to predict the tags of music recordings. However, previous music tagging research primarily focuses on close-set music tagging tasks which can not be genera…