95 citations · 229 across the 34 of their papers we have counts for
36 papers
AMemOps: Evaluating Memory Updates in Long Audio Streams
Peiwei Chang, Li Zhang, Mark D. Plumbley +2
Audio memory must recognise recurring sources across acoustic changes while keeping their attributes up to date. Overall accuracy does not reveal how these demands interact. We int…
SAIL: Spatial Audio Intelligence with Large Language Models via Disentangled Acoustic-Spatial Encoding and Dual-Stream Q-Former
Zhengding Luo, Jinyang Wu, Haozhe Ma +3
Spatial audio large language models (LLMs) enable embodied agents, wearable assistants, and immersive systems to recognize sound events, localize sources, and reason about their sp…
Exploring Second-Order Pattern Recognition in Speaker Recognition
Yanze Xu, Wenwu Wang, Mark D. Plumbley
In traditional pattern recognition tasks, neural networks are trained to recognise human-defined patterns (e.g. audio categories) in model inputs (e.g. audio). Meanwhile, some Expl…
FlowSep 2: Self-Supervised Flow Matching for Language-Queried Audio Source Separation
Yi Yuan, Xubo Liu, Haohe Liu +3
Language-queried audio source separation (LASS) aims to extract target sources from audio mixtures according to natural language descriptions, offering a flexible and scalable inte…
Efficient Text-to-Audio Generation via Pruning
Arshdeep Singh, Yi Yuan, Yun Chen +2
Diffusion-based text-to-audio generative models such as AudioLDM achieve high perceptual quality and strong semantic consistency; however, their practical deployment is hindered by…
Explainable AI in Speaker Recognition -- Attention Map Visualisation and Evaluation
Yanze Xu, Mark D. Plumbley, Wenwu Wang
Explaining and understanding the decision-making process of artificial intelligence (AI) systems, particularly those implemented by neural networks, falls within the field of expla…
Explainable AI in Speaker Recognition -- Making Latent Representations Understandable
Yanze Xu, Wenwu Wang, Mark D. Plumbley
Neural networks can be trained to learn task-relevant representations from data. Understanding how these networks make decisions falls within the Explainable AI (XAI) domain. This…
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…
Region-Specific Audio Tagging for Spatial Sound
Jinzheng Zhao, Yong Xu, Haohe Liu +6
Audio tagging aims to label sound events appearing in an audio recording. In this paper, we propose region-specific audio tagging, a new task which labels sound events in a given r…
DreamAudio: Customized Text-to-Audio Generation with Diffusion Models
Yi Yuan, Xubo Liu, Haohe Liu +5
With the development of large-scale diffusion-based and language-modeling-based generative models, impressive progress has been achieved in text-to-audio generation. Despite produc…
AudioTurbo: Fast Text-to-Audio Generation with Rectified Diffusion
Junqi Zhao, Jinzheng Zhao, Haohe Liu +5
Diffusion models have significantly improved the quality and diversity of audio generation but are hindered by slow inference speed. Rectified flow enhances inference speed by lear…
EnvSDD: Benchmarking Environmental Sound Deepfake Detection
Han Yin, Yang Xiao, Rohan Kumar Das +4
Audio generation systems now create very realistic soundscapes that can enhance media production, but also pose potential risks. Several studies have examined deepfakes in speech o…
Exploring the User Experience of AI-Assisted Sound Searching Systems for Creative Workflows
Haohe Liu, Thomas Deacon, Wenwu Wang +2
Locating the right sound effect efficiently is an important yet challenging topic for audio production. Most current sound-searching systems rely on pre-annotated audio labels crea…
Audio-Visual Class-Incremental Learning for Fish Feeding intensity Assessment in Aquaculture
Meng Cui, Xianghu Yue, Xinyuan Qian +5
Fish Feeding Intensity Assessment (FFIA) is crucial in industrial aquaculture management. Recent multi-modal approaches have shown promise in improving FFIA robustness and efficien…
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…