10 citations · 10 across the 6 of their papers we have counts for
7 papers
Membership Inference Attack Against Music Diffusion Models via Generative Manifold Perturbation
Yuxuan Liu, Peihong Zhang, Rui Sang +4
Membership inference attacks (MIAs) test whether a specific audio clip was used to train a model, making them a key tool for auditing generative music models for copyright complian…
TopSeg: A Multi-Scale Topological Framework for Data-Efficient Heart Sound Segmentation
Peihong Zhang, Zhixin Li, Yuxuan Liu +4
Deep learning approaches for heart-sound (PCG) segmentation built on time-frequency features can be accurate but often rely on large expert-labeled datasets, limiting robustness an…
DDSC: Dynamic Dual-Signal Curriculum for Data-Efficient Acoustic Scene Classification under Domain Shift
Peihong Zhang, Yuxuan Liu, Rui Sang +4
Acoustic scene classification (ASC) suffers from device-induced domain shift, especially when labels are limited. Prior work focuses on curriculum-based training schedules that str…
Training a Perceptual Model for Evaluating Auditory Similarity in Music Adversarial Attack
Yuxuan Liu, Rui Sang, Peihong Zhang +2
Music Information Retrieval (MIR) systems are highly vulnerable to adversarial attacks that are often imperceptible to humans, primarily due to a misalignment between model feature…
MAIA: An Inpainting-Based Approach for Music Adversarial Attacks
Yuxuan Liu, Peihong Zhang, Rui Sang +2
Music adversarial attacks have garnered significant interest in the field of Music Information Retrieval (MIR). In this paper, we present Music Adversarial Inpainting Attack (MAIA)…
NMCSE: Noise-Robust Multi-Modal Coupling Signal Estimation Method via Optimal Transport for Cardiovascular Disease Detection
Peihong Zhang, Zhixin Li, Rui Sang +4
The coupling signal refers to a latent physiological signal that characterizes the transformation from cardiac electrical excitation, captured by the electrocardiogram (ECG), to me…
TF-SepNet: An Efficient 1D Kernel Design in CNNs for Low-Complexity Acoustic Scene Classification
Yiqiang Cai, Peihong Zhang, Shengchen Li
Recent studies focus on developing efficient systems for acoustic scene classification (ASC) using convolutional neural networks (CNNs), which typically consist of consecutive kern…