8 citations · 14 across the 13 of their papers we have counts for
16 papers
DriftTTS: Few-Step Text-to-Speech Without Distillation via Distribution-Matching Drift
Mohammad Nur Hossain Khan, Subrata Biswas, Bashima Islam
Few-step neural text-to-speech models often rely on short- ened diffusion or flow-matching schedules, or on distillation from pretrained multi-step teachers. To avoid these depen-…
BeatGraph: Self-Supervised Heartbeat Graphs for Infant ECG Representations from the Home Environment
Mohammad Nur Hossain Khan, M. S. Krafczyk, Beverly G. Bolster +3
Electrocardiogram (ECG) foundation models typically tokenize the signal into fixed-length patches that ignore cardiac structure, so a patch may split a heartbeat and the number of…
Robust Multi-Tier Infant-Centered Audio Understanding with Whisper via Structured Speaker Conditioning
Xulin Fan, Jialu Li, Mohammad Nur Hossain Khan +4
Recent advances in model design and self-supervised audio representations have improved speech and audio understanding, yet infant-centered naturalistic recordings remain challengi…
ProcAgent: An Agentic Framework for Procedural Task Guidance on Edge with Human-in-the-Loop
Azizul Zahid, Subrata Biswas, Bashima Islam +1
Procedural tasks such as furniture assembly and home repair impose substantial cognitive demands because users must interpret instructions, track task progress, reason about spatia…
OWL: Geometry-Aware Spatial Reasoning for Audio Large Language Models
Subrata Biswas, Mohammad Nur Hossain Khan, Bashima Islam
Spatial reasoning is fundamental to auditory perception, yet current audio large language models (ALLMs) largely rely on unstructured binaural cues and single step inference. This…
Mindfulness Meditation and Respiration: Accelerometer-Based Respiration Rate and Mindfulness Progress Estimation to Enhance App Engagement and Mindfulness Skills
Mohammad Nur Hossain Khan, David creswell, Jordan Albert +5
Mindfulness training is widely recognized for its benefits in reducing depression, anxiety, and loneliness. With the rise of smartphone-based mindfulness apps, digital meditation h…
UnIT: Scalable Unstructured Inference-Time Pruning for MAC-efficient Neural Inference on MCUs
Ashe Neth, Sawinder kaur, Mohammad Nur Hossain Khan +3
Existing pruning methods are typically applied during training or compile time and often rely on structured sparsity. While compatible with low-power microcontrollers (MCUs), struc…
QUADS: QUAntized Distillation Framework for Efficient Speech Language Understanding
Subrata Biswas, Mohammad Nur Hossain Khan, Bashima Islam
Spoken Language Understanding (SLU) systems must balance performance and efficiency, particularly in resource-constrained environments. Existing methods apply distillation and quan…
RAVEN: Query-Guided Representation Alignment for Question Answering over Audio, Video, Embedded Sensors, and Natural Language
Subrata Biswas, Mohammad Nur Hossain Khan, Bashima Islam
Multimodal question answering (QA) often requires identifying which video, audio, or sensor tokens are relevant to the question. Yet modality disagreements are common: off-camera s…
Sound Tagging in Infant-centric Home Soundscapes
Mohammad Nur Hossain Khan, Jialu Li, Nancy L. McElwain +2
Certain environmental noises have been associated with negative developmental outcomes for infants and young children. Though classifying or tagging sound events in a domestic envi…
Missingness-resilient Video-enhanced Multimodal Disfluency Detection
Payal Mohapatra, Shamika Likhite, Subrata Biswas +2
Most existing speech disfluency detection techniques only rely upon acoustic data. In this work, we present a practical multimodal disfluency detection approach that leverages avai…
LLaSA: A Sensor-Aware LLM for Natural Language Reasoning of Human Activity from IMU Data
Sheikh Asif Imran, Mohammad Nur Hossain Khan, Subrata Biswas +1
Wearable systems can recognize activities from IMU data but often fail to explain their underlying causes or contextual significance. To address this limitation, we introduce two l…
Memory-efficient Energy-adaptive Inference of Pre-Trained Models on Batteryless Embedded Systems
Pietro Farina, Subrata Biswas, Eren Yıldız +4
Batteryless systems frequently face power failures, requiring extra runtime buffers to maintain inference progress and leaving only a memory space for storing ultra-tiny deep neura…
Classification of Infant Sleep/Wake States: Cross-Attention among Large Scale Pretrained Transformer Networks using Audio, ECG, and IMU Data
Kai Chieh Chang, Mark Hasegawa-Johnson, Nancy L. McElwain +1
Infant sleep is critical to brain and behavioral development. Prior studies on infant sleep/wake classification have been largely limited to reliance on expensive and burdensome po…
Amalgamated Intermittent Computing Systems
Bashima Islam, Yubo Luo, Shahriar Nirjon
Intermittent computing systems undergo frequent power failure, hindering necessary data sample capture or timely on-device computation. These missing samples and deadlines limit th…
LOCUS: LOcalization with Channel Uncertainty and Sporadic Energy
Subrata Biswas, Mohammad Nur Hossain Khan, Violet Colwell +2
Accurate sound source localization (SSL), such as direction-of-arrival (DoA) estimation, relies on consistent multichannel data. However, batteryless systems often suffer from miss…