5 papers · 1 filter
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…
Resonate: Reinforcing Text-to-Audio Generation via Online Feedback from Large Audio Language Models
Xiquan Li, Junxi Liu, Wenxi Chen +3
Reinforcement Learning (RL) has become an effective paradigm for enhancing Large Language Models (LLMs) and visual generative models. However, its application in text-to-audio (TTA…
MeanAudio: Fast and Faithful Text-to-Audio Generation with Mean Flows
Xiquan Li, Junxi Liu, Yuzhe Liang +3
Recent years have witnessed remarkable progress in Text-to-Audio Generation (TTA), providing sound creators with powerful tools to transform inspirations into vivid audio. Yet desp…
DRCap: Decoding CLAP Latents with Retrieval-Augmented Generation for Zero-shot Audio Captioning
Xiquan Li, Wenxi Chen, Ziyang Ma +5
While automated audio captioning (AAC) has made notable progress, traditional fully supervised AAC models still face two critical challenges: the need for expensive audio-text pair…
EmoBox: Multilingual Multi-corpus Speech Emotion Recognition Toolkit and Benchmark
Ziyang Ma, Mingjie Chen, Hezhao Zhang +6
Speech emotion recognition (SER) is an important part of human-computer interaction, receiving extensive attention from both industry and academia. However, the current research fi…