16 citations · 18 across the 8 of their papers we have counts for
9 papers
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…
Automated Analysis of Naturalistic Recordings in Early Childhood: Applications, Challenges, and Opportunities
Jialu Li, Marvin Lavechin, Xulin Fan +4
Naturalistic recordings capture audio in real-world environments where participants behave naturally without interference from researchers or experimental protocols. Naturalistic l…
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…
Analysis of Self-Supervised Speech Models on Children's Speech and Infant Vocalizations
Jialu Li, Mark Hasegawa-Johnson, Nancy L. McElwain
To understand why self-supervised learning (SSL) models have empirically achieved strong performances on several speech-processing downstream tasks, numerous studies have focused o…
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…
Towards Robust Family-Infant Audio Analysis Based on Unsupervised Pretraining of Wav2vec 2.0 on Large-Scale Unlabeled Family Audio
Jialu Li, Mark Hasegawa-Johnson, Nancy L. McElwain
To perform automatic family audio analysis, past studies have collected recordings using phone, video, or audio-only recording devices like LENA, investigated supervised learning m…
Visualizations of Complex Sequences of Family-Infant Vocalizations Using Bag-of-Audio-Words Approach Based on Wav2vec 2.0 Features
Jialu Li, Mark Hasegawa-Johnson, Nancy L. McElwain
In the U.S., approximately 15-17% of children 2-8 years of age are estimated to have at least one diagnosed mental, behavioral or developmental disorder. However, such disorders of…
A Comparison Study on Infant-Parent Voice Diarization
Junzhe Zhu, Mark Hasegawa-Johnson, Nancy McElwain
We design a framework for studying prelinguistic child voicefrom 3 to 24 months based on state-of-the-art algorithms in di-arization. Our system consists of a time-invariant featur…