The rapid adoption of ear-worn devices (earables) has shown significant potential for continuous health monitoring. Despite their close proximity to the human brain and diverse sensing capabilities, the exploration of earable sensing in relation to cognitive function remains underexplored. Building on theoretical and empirical foundations regarding the interplay between cognitive load, auditory complexity, and changes in hearing characteristics influenced by brain function, this study is the first to leverage earable acoustic sensing to assess cognitive load. We specifically designed auditory tasks to elicit four levels of cognitive load and used otoacoustic emissions (OAEs) to measure cochlear response changes in response to cognitive load. By utilizing both audio content indicating auditory complexity and OAEs reflecting hearing characteristic changes, we designed machine learning pipelines to automate the assessment in a four-class cognitive detection task, achieving an accuracy of 68.88%. This research opens a new pathway for using earable acoustic sensing in monitoring cognitive function and holds great potential for future cognitive augmentation.

In a world dominated by podcasts and audiobooks, maintaining auditory attention is essential, yet lapses in focus are common. Auditory attention is crucial for effective communication and comprehension in a distraction-filled environment, as it enables us to focus on important sounds while avoiding external distractions. This work introduces a novel, imperceptible method for detecting auditory attention using earbuds by monitoring muscle movement within the ear canal. We employ an ultrasound-based sensing technique to track phase changes in reflected signals, detecting muscle vibrations associated with shifts in attention. A preliminary user study reveals significant changes in in-ear signal characteristics when participants switch between auditory and cognitive tasks. We show that our system can classify periods of auditory attention and lack of it with an accuracy of 85.7% and a variance of 0.0033. Our findings pave the way for earables that continuously monitor and enhance auditory attention in real-time.

Wearable technology is advancing health monitoring by enabling real-time, privacy-preserving physiological analysis. However, traditional devices often rely on cloud processing, restricting access to raw sensor data and limiting the progress of health-related research. To overcome these limitations, we introduce OmniBuds, a programmable earable research platform that enables multimodal bio-sensing and on-device learning while providing direct access to raw physiological data, fostering advancements in health perception and wearable intelligence. It integrates PPG, temperature, IMUs, and multiple microphones, leveraging an embedded ML accelerator for efficient real-time processing. This paper presents its design, architecture, and applications, demonstrating its potential to shape the future of health-aware wearables.

Distortion Product OtoAcoustic Emissions (DPOAEs) represents faint cochlear responses to dual-frequency stimuli, commonly employed in hearing screening. This paper introduces an innovative approach to trigger DPOAEs using single-speaker earbuds. Due to their compact size, the speakers used in the earbuds exhibit nonlinear behavior, leading to Inter-Modulation Distortions (IMDs) that interfere with DPOAE signals. Conventional medical devices employ dual speakers to mitigate this distortion, such a solution is impractical for space-constrained earbuds. To address this challenge, we propose a method that triggers DPOAEs while circumventing IMDs by designing a stimulus signal that alternates between the two frequencies necessary for triggering DPOAEs. The performance of our system was evaluated through a preliminary user study involving 8 participants, and it demonstrated a median correlation of 0.65 when compared to a medical-grade reference device.

This paper presents a novel cancellation method of Intermodulation Distortions (IMDs) for earbud speakers used to measure Distortion Product Otoacoustic Emissions (DPOAE). Speakers’ non-linear behaviour is a significant problem for earbuds with small loudspeakers due to limitations in cone movement. Linear and non-linear speaker modelling enables us to inject exact distortion inverse to cancel what is introduced by speakers’ non-linearities. Our proposed method is compared against state-of-the-art related works in terms of harmonic reduction ratio. Simulation results and evaluation on real hardware show a 77% to 95% reduction in the harmonic distortions of a focused frequency region at the output of the loudspeaker, outperforming existing works by 6% to 14%.

Applications such as transaction authentication may require speaker recognition systems to operate on compressed speech transmitted over mobile phone networks. However, speech compression degrades speech quality, and hence causes a reduction in recognition performance. It has been shown that the classic technique for extraction of line spectral frequency (LSF) parameters in speech coders is prone to aliasing distortion. The use of a low-pass filtering on upsampled LSF vectors has been shown to alleviate this problem, therefore improving speech quality. In this paper, the effect of this non-aliased LSF (NA-LSF) extraction method on speaker recognition performance is observed using GSM-EFR coded speech. When the NA-LSFs are used in the coder the verification performance loss introduced by the GSM-EFR is reduced, producing similar performance to uncoded speech

This paper presents the behavioural mechanism of a statistical model-based voice activity detector (VAD), featuring a likelihood ratio test for the activity decision. From investigation of the VAD, it is found that detection errors could occur frequently at speech offset regions because of the delay term in the decision-directed parameter estimator, employed for the estimation of an unknown parameter of the likelihood ratio. Hence, this paper proposes a smoothed likelihood ratio so as to alleviate the detection errors at the offset region. Objective test results show that the proposed scheme is useful for achieving a considerable performance improvement for the VAD. Additionally, the proposed VAD gives detection performances superior to G.729B VAD and comparable with the AMR VAD option 2.