77 citations · 81 across the 15 of their papers we have counts for
15 papers
Device Passport: Enabling Spatio-Temporal Pretrained Models to Generalize Across Input Layouts
Geeling Chau, Ran Liu, Juri Minxha +5
New device layouts pose a challenging modeling problem due to the lack of large datasets for each specific layout. Biosignal foundation models offer a plausible solution if they ar…
Learning the relative composition of EEG signals using pairwise relative shift pretraining
Christopher Sandino, Sayeri Lala, Geeling Chau +6
Self-supervised learning (SSL) offers a promising approach for learning electroencephalography (EEG) representations from unlabeled data, reducing the need for expensive annotation…
CPEP: Contrastive Pose-EMG Pre-training Enhances Gesture Generalization on EMG Signals
Wenhui Cui, Christopher Sandino, Hadi Pouransari +7
Hand gesture classification using high-quality structured data such as videos, images, and hand skeletons is a well-explored problem in computer vision. Leveraging low-power, cost-…
Foundation Model Hidden Representations for Heart Rate Estimation from Auscultation
Jingping Nie, Dung T. Tran, Karan Thakkar +5
Auscultation, particularly heart sound, is a non-invasive technique that provides essential vital sign information. Recently, self-supervised acoustic representation foundation mod…
Modeling speech emotion with label variance and analyzing performance across speakers and unseen acoustic conditions
Vikramjit Mitra, Amrit Romana, Dung T. Tran +1
Spontaneous speech emotion data usually contain perceptual grades where graders assign emotion score after listening to the speech files. Such perceptual grades introduce uncertain…
Promoting cross-modal representations to improve multimodal foundation models for physiological signals
Ching Fang, Christopher Sandino, Behrooz Mahasseni +5
Many healthcare applications are inherently multimodal, involving several physiological signals. As sensors for these signals become more common, improving machine learning methods…
Generalizable autoregressive modeling of time series through functional narratives
Ran Liu, Wenrui Ma, Ellen Zippi +8
Time series data are inherently functions of time, yet current transformers often learn time series by modeling them as mere concatenations of time periods, overlooking their funct…
Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement
Gaurav Patel, Christopher Sandino, Behrooz Mahasseni +4
In this paper, we propose a framework for efficient Source-Free Domain Adaptation (SFDA) in the context of time-series, focusing on enhancing both parameter efficiency and data-sam…
Model-driven Heart Rate Estimation and Heart Murmur Detection based on Phonocardiogram
Jingping Nie, Ran Liu, Behrooz Mahasseni +2
Acoustic signals are crucial for health monitoring, particularly heart sounds which provide essential data like heart rate and detect cardiac anomalies such as murmurs. This study…
Pre-Trained Foundation Model representations to uncover Breathing patterns in Speech
Vikramjit Mitra, Anirban Chatterjee, Ke Zhai +6
The process of human speech production involves coordinated respiratory action to elicit acoustic speech signals. Typically, speech is produced when air is forced from the lungs an…
Investigating salient representations and label Variance in Dimensional Speech Emotion Analysis
Vikramjit Mitra, Jingping Nie, Erdrin Azemi
Representations derived from models such as BERT (Bidirectional Encoder Representations from Transformers) and HuBERT (Hidden units BERT), have helped to achieve state-of-the-art p…
Frequency-Aware Masked Autoencoders for Multimodal Pretraining on Biosignals
Ran Liu, Ellen L. Zippi, Hadi Pouransari +5
Leveraging multimodal information from biosignals is vital for building a comprehensive representation of people's physical and mental states. However, multimodal biosignals often…
Pre-trained Model Representations and their Robustness against Noise for Speech Emotion Analysis
Vikramjit Mitra, Vasudha Kowtha, Hsiang-Yun Sherry Chien +2
Pre-trained model representations have demonstrated state-of-the-art performance in speech recognition, natural language processing, and other applications. Speech models, such as…
Speech Emotion: Investigating Model Representations, Multi-Task Learning and Knowledge Distillation
Vikramjit Mitra, Hsiang-Yun Sherry Chien, Vasudha Kowtha +2
Estimating dimensional emotions, such as activation, valence and dominance, from acoustic speech signals has been widely explored over the past few years. While accurate estimation…
Subject-Aware Contrastive Learning for Biosignals
Joseph Y. Cheng, Hanlin Goh, Kaan Dogrusoz +2
Datasets for biosignals, such as electroencephalogram (EEG) and electrocardiogram (ECG), often have noisy labels and have limited number of subjects (<100). To handle these challen…