Open portable platform for hearing aid research

Pavlovic, Caslav; Hohmann, Volker; Kayser, Hendrik; Wong, Louis; Herzke, Tobias; Prakash, S. R.; Hou, Zezhang; Maanen, Paul

doi:10.1121/1.5035670

Cited by 8 publications

(4 citation statements)

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“…The eSense platform [9,51] has enabled research in sensing applications with earables. OpenMHA [59,60] is an open signal processing software platform for hearing aid research. Neither of these platforms support time-synchronized audio transmission from two earbuds, which is a critical requirement for achieving speech enhancement in binaural settings.…”

Section: Related Workmentioning

confidence: 99%

ClearBuds

Chatterjee

Kim

Jayaram

et al. 2022

Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services

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We present ClearBuds, the first hardware and software system that utilizes a neural network to enhance speech streamed from two wireless earbuds. Real-time speech enhancement for wireless earbuds requires high-quality sound separation and background cancellation, operating in real-time and on a mobile phone. Clear-Buds bridges state-of-the-art deep learning for blind audio source separation and in-ear mobile systems by making two key technical contributions: 1) a new wireless earbud design capable of operating as a synchronized, binaural microphone array, and 2) a lightweight dual-channel speech enhancement neural network that runs on a mobile device. Our neural network has a novel cascaded architecture that combines a time-domain conventional neural network with a spectrogram-based frequency masking neural network to reduce the artifacts in the audio output. Results show that our wireless earbuds achieve a synchronization error less than 64 𝜇s and our network has a runtime of 21.4 ms on an accompanying mobile phone. In-the-wild evaluation with eight users in previously unseen indoor and outdoor multipath scenarios demonstrates that our neural network generalizes to learn both spatial and acoustic cues to perform noise suppression and background speech removal. In a user-study with 37 participants who spent over 15.4 hours rating 1041 audio samples collected in-the-wild, our system achieves improved mean opinion score and background noise suppression. System demo video: https://youtu.be/HYu0ybjcQPA CCS CONCEPTS• Computer systems organization → Embedded systems; • Human-centered computing → Ubiquitous and mobile computing systems and tools; • Computing methodologies → Machine learning.

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Section: Related Workmentioning

confidence: 99%

ClearBuds

Chatterjee

Kim

Jayaram

et al. 2022

Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services

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“…In combination with a software suite that provides signal processing capabilities as well as measurement applications for hearing research a complete field test system is available. See Section 3 for an example of such a field test setup that is tailored to hearing device research with openMHA [33].…”

Section: Motivation and Significancementioning

confidence: 99%

“…One example of how openMHA can be used in the field the "Portable Hearing Laboratory" (PHL, [33]), see Figure 2. The PHL consist of a BeagleBone Balck wireless single-board computer that was extended with a multi-channel audio board based on the open-source audio interface Cape4all [55].…”

Section: Illustrative Examplementioning

confidence: 99%

Open community platform for hearing aid algorithm research: open Master Hearing Aid (openMHA)

Kayser,

Herzke,

Maanen

et al. 2021

Preprint

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open Master Hearing Aid (openMHA) was developed and provided to the hearing aid research community as an open-source software platform with the aim to support sustainable and reproducible research towards improvement and new types of assistive hearing systems not limited by proprietary software. The software offers a flexible framework that allows the users to conduct hearing aid research using tools and a number of signal processing plugins provided with the software as well as the implementation of own methods. The openMHA software is independent of a specific hardware and supports Linux, MacOS and Windows operating systems as well as 32-bit and 64-bit ARM-based architectures such as used in small portable integrated systems. www.openmha.org

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“…To develop EEG-based hearing aid signal processing methods, a portable platform is required that offers the signal quality and timing accuracy required for closed-loop applications with audio and EEG signals. In Dasenbrock et al ( 2021 ), we introduced a research platform, which combines the mobile Smarting EEG system and the so-called Portable Hearing Laboratory (PHL, Pavlovic et al, 2018 ). The PHL is a portable research hearing device that can fully replicate a hearing device.…”

Section: Introductionmentioning

confidence: 99%

Synchronization of ear-EEG and audio streams in a portable research hearing device

et al. 2022

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Recent advancements in neuroscientific research and miniaturized ear-electroencephalography (EEG) technologies have led to the idea of employing brain signals as additional input to hearing aid algorithms. The information acquired through EEG could potentially be used to control the audio signal processing of the hearing aid or to monitor communication-related physiological factors. In previous work, we implemented a research platform to develop methods that utilize EEG in combination with a hearing device. The setup combines currently available mobile EEG hardware and the so-called Portable Hearing Laboratory (PHL), which can fully replicate a complete hearing aid. Audio and EEG data are synchronized using the Lab Streaming Layer (LSL) framework. In this study, we evaluated the setup in three scenarios focusing particularly on the alignment of audio and EEG data. In Scenario I, we measured the latency between software event markers and actual audio playback of the PHL. In Scenario II, we measured the latency between an analog input signal and the sampled data stream of the EEG system. In Scenario III, we measured the latency in the whole setup as it would be used in a real EEG experiment. The results of Scenario I showed a jitter (standard deviation of trial latencies) of below 0.1 ms. The jitter in Scenarios II and III was around 3 ms in both cases. The results suggest that the increased jitter compared to Scenario I can be attributed to the EEG system. Overall, the findings show that the measurement setup can time-accurately present acoustic stimuli while generating LSL data streams over multiple hours of playback. Further, the setup can capture the audio and EEG LSL streams with sufficient temporal accuracy to extract event-related potentials from EEG signals. We conclude that our setup is suitable for studying closed-loop EEG & audio applications for future hearing aids.

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Open portable platform for hearing aid research

Cited by 8 publications

References 0 publications

ClearBuds

ClearBuds

Open community platform for hearing aid algorithm research: open Master Hearing Aid (openMHA)

Synchronization of ear-EEG and audio streams in a portable research hearing device

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