Active noise cancellation on open-ear smart glasses
Abstract
Smart glasses are becoming an increasingly prevalent wearable platform, with audio as a key interaction modality. However, hearing in noisy environments remains challenging because smart glasses are equipped with open-ear speakers that do not seal the ear canal. Furthermore, the open-ear design is incompatible with conventional active noise cancellation (ANC) techniques, which rely on an error microphone inside or at the entrance of the ear canal to measure the residual sound heard after cancellation. Here we present the first real-time ANC system for open-ear smart glasses that suppresses environmental noise using only microphones and miniaturized open-ear speakers embedded in the glasses frame. Our low-latency computational pipeline estimates the noise at the ear from an array of eight microphones distributed around the glasses frame and generates an anti-noise signal in real-time to cancel environmental noise. We develop a custom glasses prototype and evaluate it in a user study across 8 environments under mobility in the 100--1000 Hz frequency range, where environmental noise is concentrated. We achieve a mean noise reduction of 9.6 dB without any calibration, and 11.2 dB with a brief user-specific calibration.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper introduces a pioneering ANC system for open-ear smart glasses that operates without error microphones, demonstrating significant noise reduction capabilities in real-world settings. The innovative methodology and thorough experimental evaluation contribute meaningfully to the field of audio processing and wearable technology, paving the way for future advancements in auditory interfaces.
Methodology
The paper presents a novel approach to active noise cancellation (ANC) specifically designed for open-ear smart glasses, which traditionally face challenges due to their non-occlusive design. The methodology leverages a dual-pipeline architecture that separates the estimation of noise propagation and the generation of anti-noise signals, utilizing a neural network for virtual in-ear sensing. This innovative approach circumvents the need for error microphones, which are typically required in ANC systems, by estimating the sound at the ear from an array of microphones distributed around the glasses frame. The use of a custom 3D-printed prototype and the integration of a low-latency DSP unit for real-time processing further enhance the practicality of the solution.
Results & evaluation
The experimental evaluation is robust, encompassing controlled benchtop tests on a mannequin head and real-world user studies across various environments. The authors demonstrate effective noise reduction performance, achieving a mean reduction of 9.6 dB without calibration and 11.2 dB with user-specific calibration. The study involved 11 participants and assessed performance across 8 different environments, showcasing the system's adaptability to diverse acoustic conditions. The use of both objective metrics (e.g., noise reduction levels) and subjective user ratings (e.g., clarity and intrusiveness) strengthens the evaluation.
Reproducibility
The paper provides detailed descriptions of the hardware setup, including the specifications of the microphones and DSP units used, as well as the neural network architecture. However, the lack of a publicly accessible demo or project URL limits reproducibility. The authors do mention the use of a calibration procedure, which could be a barrier for replication without access to the same hardware setup.
Limitations
Key limitations include the system's reduced performance in outdoor environments due to wind noise, which the authors acknowledge as a significant challenge for open-ear designs. Additionally, the reliance on a brief calibration procedure may not be feasible for all users, particularly if the glasses shift during extended wear. The neural network's filter update rate of 200 ms could also hinder responsiveness to rapid changes in the acoustic environment.
Broader impact
The potential applications of this research extend beyond smart glasses to other open-ear wearables, such as augmented and virtual reality headsets. The ability to enhance audio clarity in noisy environments could significantly improve user experience in various contexts, including professional training and everyday use. The findings could also inform future developments in auditory interfaces and personalized hearing assistance technologies. This paper introduces a pioneering ANC system for open-ear smart glasses that operates without error microphones, demonstrating significant noise reduction capabilities in real-world settings. The innovative methodology and thorough experimental evaluation contribute meaningfully to the field of audio processing and wearable technology, paving the way for future advancements in auditory interfaces.
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