95 citations · 229 across the 34 of their papers we have counts for
7 papers · 1 filter
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…