6 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…
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…
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)…