16 citations · 30 across the 5 of their papers we have counts for
8 papers
ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition
Yash Jain, Chi Ian Tang, Chulhong Min +2
A major bottleneck in training robust Human-Activity Recognition models (HAR) is the need for large-scale labeled sensor datasets. Because labeling large amounts of sensor data is…
SensiX++: Bringing MLOPs and Multi-tenant Model Serving to Sensory Edge Devices
Chulhong Min, Akhil Mathur, Utku Gunay Acer +2
We present SensiX++ - a multi-tenant runtime for adaptive model execution with integrated MLOps on edge devices, e.g., a camera, a microphone, or IoT sensors. SensiX++ operates on…
Low-Power Audio Keyword Spotting using Tsetlin Machines
Jie Lei, Tousif Rahman, Rishad Shafik +5
The emergence of Artificial Intelligence (AI) driven Keyword Spotting (KWS) technologies has revolutionized human to machine interaction. Yet, the challenge of end-to-end energy ef…
SensiX: A Platform for Collaborative Machine Learning on the Edge
Chulhong Min, Akhil Mathur, Alessandro Montanari +2
The emergence of multiple sensory devices on or near a human body is uncovering new dynamics of extreme edge computing. In this, a powerful and resource-rich edge device such as a…
Libri-Adapt: A New Speech Dataset for Unsupervised Domain Adaptation
Akhil Mathur, Fahim Kawsar, Nadia Berthouze +1
This paper introduces a new dataset, Libri-Adapt, to support unsupervised domain adaptation research on speech recognition models. Built on top of the LibriSpeech corpus, Libri-Ada…
Mic2Mic: Using Cycle-Consistent Generative Adversarial Networks to Overcome Microphone Variability in Speech Systems
Akhil Mathur, Anton Isopoussu, Fahim Kawsar +2
Mobile and embedded devices are increasingly using microphones and audio-based computational models to infer user context. A major challenge in building systems that combine audio…