95 citations · 229 across the 34 of their papers we have counts for
16 papers · 1 filter
SyncFlow: Toward Temporally Aligned Joint Audio-Video Generation from Text
Haohe Liu, Gael Le Lan, Xinhao Mei +7
Video and audio are closely correlated modalities that humans naturally perceive together. While recent advancements have enabled the generation of audio or video from text, produc…
AudioSetCaps: An Enriched Audio-Caption Dataset using Automated Generation Pipeline with Large Audio and Language Models
Jisheng Bai, Haohe Liu, Mou Wang +5
With the emergence of audio-language models, constructing large-scale paired audio-language datasets has become essential yet challenging for model development, primarily due to th…
PSELDNets: Pre-trained Neural Networks on a Large-scale Synthetic Dataset for Sound Event Localization and Detection
Jinbo Hu, Yin Cao, Ming Wu +5
Sound event localization and detection (SELD) has seen substantial advancements through learning-based methods. These systems, typically trained from scratch on specific datasets,…
FlowSep: Language-Queried Sound Separation with Rectified Flow Matching
Yi Yuan, Xubo Liu, Haohe Liu +2
Language-queried audio source separation (LASS) focuses on separating sounds using textual descriptions of the desired sources. Current methods mainly use discriminative approaches…
Efficient Audio Captioning with Encoder-Level Knowledge Distillation
Xuenan Xu, Haohe Liu, Mengyue Wu +2
Significant improvement has been achieved in automated audio captioning (AAC) with recent models. However, these models have become increasingly large as their performance is enhan…
Universal Sound Separation with Self-Supervised Audio Masked Autoencoder
Junqi Zhao, Xubo Liu, Jinzheng Zhao +4
Universal sound separation (USS) is a task of separating mixtures of arbitrary sound sources. Typically, universal separation models are trained from scratch in a supervised manner…
Sound-VECaps: Improving Audio Generation with Visual Enhanced Captions
Yi Yuan, Dongya Jia, Xiaobin Zhuang +9
Generative models have shown significant achievements in audio generation tasks. However, existing models struggle with complex and detailed prompts, leading to potential performan…
Learning Retrieval Augmentation for Personalized Dialogue Generation
Qiushi Huang, Shuai Fu, Xubo Liu +4
Personalized dialogue generation, focusing on generating highly tailored responses by leveraging persona profiles and dialogue context, has gained significant attention in conversa…
Selective Prompting Tuning for Personalized Conversations with LLMs
Qiushi Huang, Xubo Liu, Tom Ko +4
In conversational AI, personalizing dialogues with persona profiles and contextual understanding is essential. Despite large language models' (LLMs) improved response coherence, ef…
Text-Queried Target Sound Event Localization
Jinzheng Zhao, Xinyuan Qian, Yong Xu +4
Sound event localization and detection (SELD) aims to determine the appearance of sound classes, together with their Direction of Arrival (DOA). However, current SELD systems can o…
Zero-Shot Audio Captioning Using Soft and Hard Prompts
Yiming Zhang, Xuenan Xu, Ruoyi Du +5
In traditional audio captioning methods, a model is usually trained in a fully supervised manner using a human-annotated dataset containing audio-text pairs and then evaluated on t…
Fish Tracking, Counting, and Behaviour Analysis in Digital Aquaculture: A Comprehensive Survey
Meng Cui, Xubo Liu, Haohe Liu +3
Digital aquaculture leverages advanced technologies and data-driven methods, providing substantial benefits over traditional aquaculture practices. This paper presents a comprehens…
SemantiCodec: An Ultra Low Bitrate Semantic Audio Codec for General Sound
Haohe Liu, Xuenan Xu, Yi Yuan +3
Large language models (LLMs) have significantly advanced audio processing through audio codecs that convert audio into discrete tokens, enabling the application of language modelli…
ComposerX: Multi-Agent Symbolic Music Composition with LLMs
Qixin Deng, Qikai Yang, Ruibin Yuan +16
Music composition represents the creative side of humanity, and itself is a complex task that requires abilities to understand and generate information with long dependency and har…
T-CLAP: Temporal-Enhanced Contrastive Language-Audio Pretraining
Yi Yuan, Zhuo Chen, Xubo Liu +6
Contrastive language-audio pretraining~(CLAP) has been developed to align the representations of audio and language, achieving remarkable performance in retrieval and classificatio…
WavCraft: Audio Editing and Generation with Large Language Models
Jinhua Liang, Huan Zhang, Haohe Liu +7
We introduce WavCraft, a collective system that leverages large language models (LLMs) to connect diverse task-specific models for audio content creation and editing. Specifically,…