1 citations · 3 across the 7 of their papers we have counts for
8 papers
Role-guided Speaker Deletion Verification in Clinical Psychiatry Speech Recordings with Audio Language Models
Joseph T Colonel, Daniel Katzman, Kelsey Kirker +6
Clinical research in psychiatry increasingly relies on large scale collection of spoken language data to identify acoustic and linguistic biomarkers. Yet evolving consent and proto…
Word Clouds as Common Voices: LLM-Assisted Visualization of Participant-Weighted Themes in Qualitative Interviews
Joseph T. Colonel, Baihan Lin
Word clouds are a common way to summarize qualitative interviews, yet traditional frequency-based methods often fail in conversational contexts: they surface filler words, ignore p…
CAtCh: Cognitive Assessment through Cookie Thief
Joseph T Colonel, Carolyn Hagler, Guiselle Wismer +5
Several machine learning algorithms have been developed for the prediction of Alzheimer's disease and related dementia (ADRD) from spontaneous speech. However, none of these algori…
SoftStep: Learning Sparse Similarity Powers Deep Neighbor-Based Regression
Aviad Susman, Baihan Lin, Mayte Suárez-Fariñas +1
Neighbor-based methods are a natural alternative to linear prediction for tabular data when relationships between inputs and targets exhibit complexity such as nonlinearity, period…
Predicting Cognitive Decline: A Multimodal AI Approach to Dementia Screening from Speech
Lei Chi, Arav Sharma, Ari Gebhardt +1
Recent progress has been made in detecting early stage dementia entirely through recordings of patient speech. Multimodal speech analysis methods were applied to the PROCESS challe…
Direct design of biquad filter cascades with deep learning by sampling random polynomials
Joseph T. Colonel, Christian J. Steinmetz, Marcus Michelen +1
Designing infinite impulse response filters to match an arbitrary magnitude response requires specialized techniques. Methods like modified Yule-Walker are relatively efficient, bu…
Autoencoding Neural Networks as Musical Audio Synthesizers
Joseph Colonel, Christopher Curro, Sam Keene
A method for musical audio synthesis using autoencoding neural networks is proposed. The autoencoder is trained to compress and reconstruct magnitude short-time Fourier transform f…
Conditioning Autoencoder Latent Spaces for Real-Time Timbre Interpolation and Synthesis
Joseph T Colonel, Sam Keene
We compare standard autoencoder topologies' performances for timbre generation. We demonstrate how different activation functions used in the autoencoder's bottleneck distributes a…