Work

A Resource Efficient System for On-Smartwatch Audio Processing

Year: 2024

Type: conference-paper

Abstract: While audio data shows promise in addressing various health challenges, there is a lack of research on on-device audio processing for smartwatches. Privacy concerns make storing raw audio and performing post-hoc analysis undesirable for many users. Additionally, current on-device audio processing systems for smartwatches are limited in their feature extraction capabilities, restricting their potential for understanding user behavior and health. We developed a real-time system for on-device audio processing on smartwatches, which takes an average of 1.78 minutes (SD = 0.07 min) to extract 22 spectral and rhythmic features from a 1-minute audio sample, using a small window size of 25 milliseconds. Using these extracted audio features on a public dataset, we developed and incorporated models into a watch to classify foreground and background speech in real-time. Our Random Forest-based model classifies speech with a balanced accuracy of 80.3%.

Source: Proceedings of the 28th Annual International Conference on Mobile Computing And Networking

DOI: 10.1145/3636534.3698866

Authors: Md Sabbir Ahmed, Arafat Rahman, Zhiyuan Wang, Mark Rucker, Laura E. Barnes

Institution: University of Virginia

Language: English

Topic: Speech and Audio Processing

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Institutes of Health, National Institute of Mental Health

Award: G5688118063