Manh Pham Hung

Manh Pham Hung, Changshuo Hu, Ting Dang et al.

Device-guided music transfer adapts playback across unseen devices for users who lack them. Existing methods mainly focus on modifying the timbre, rhythm, harmony, or instrumentation to mimic genres or artists, overlooking the diverse hardware properties of the playback device (i.e., speaker). Therefore, we propose DeMT, which processes a speaker's frequency response curve as a line graph using a vision-language model to extract device embeddings. These embeddings then condition a hybrid transformer via feature-wise linear modulation. Fine-tuned on a self-collected dataset, DeMT enables effective speaker-style transfer and robust few-shot adaptation for unseen devices, supporting applications like device-style augmentation and quality enhancement.

Manh Pham Hung, Matthew Yiwen Ho, Yiming Zhang et al.

Photoplethysmography (PPG) is a widely adopted, non-invasive technique for monitoring cardiovascular health and physiological parameters in both consumer and clinical settings. While motion artifacts in dynamic environments have been extensively studied, suboptimal skin-sensor contact in sedentary conditions - a critical yet underexplored issue - can distort PPG waveform morphology, leading to the loss or misalignment of key features and compromising sensing accuracy. In this work, we propose CP-PPG, a novel framework that transforms Contact Pressure-distorted PPG signals into high-fidelity waveforms with ideal morphology. CP-PPG integrates a custom data collection protocol, a carefully designed signal processing pipeline, and a novel deep adversarial model trained with a custom PPG-aware loss function. We validated CP-PPG through comprehensive evaluations, including 1) morphology transformation performance on our self-collected dataset, 2) downstream physiological monitoring performance on public datasets, and 3) in-the-wild study. Extensive experiments demonstrate substantial and consistent improvements in signal fidelity (Mean Absolute Error: 0.09, 40% improvement over the original signal) as well as downstream performance across all evaluations in Heart Rate (HR), Heart Rate Variability (HRV), Respiration Rate (RR), and Blood Pressure (BP) estimation (on average, 21% improvement in HR; 41-46% in HRV; 6% in RR; and 4-5% in BP). These findings highlight the critical importance of addressing skin-sensor contact issues to enhance the reliability and effectiveness of PPG-based physiological monitoring. CP-PPG thus holds significant potential to improve the accuracy of wearable health technologies in clinical and consumer applications.