A Realtime, Open-Source Speech-Processing Platform for Research in Hearing Loss Compensation

A High-Performance, Low Power Research Hearing Aid featuring a High-Level Programmable Custom 22nm FDSOI SoC. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023;2023:1-5. [PMID: 38083506 DOI: 10.1109/embc40787.2023.10340206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

With advances in algorithmic hearing aid research, the need for high-level programmable, behind-the-ear (BTE) wearable and low-power research platforms is emerging. These can be used to test new algorithms in real-world scenarios. Although various groups are developing different portable solutions, they are not in a BTE form factor. For this reason, the devices must be worn around the neck or somewhere on the body, which causes limited mobility and can lead to inaccurate research results. Therefore, this work presents a fully integrated and functional hearing aid research platform that weighs only 5 grams and can be worn behind the ear. The platform is high-level programmable, features wireless technologies such as near-field magnetic induction (NFMI) and Bluetooth Low Energy (BLE), and integrates two micro-electro-mechanical systems (MEMS) microphones and an external speaker. The audio processor of the system is based on a new custom, low-power 22nm mixed-signal system on chip (SoC). Different real-world use cases, like a dynamic compressor, are used to evaluate the platform. With a total power consumption of 47 mW, the rechargeable device achieves a run-time of six hours. When the wireless interfaces are turned off, the power consumption drops to 31 mW, and the run-time increases to nine hours.Clinical relevance-The proposed research hearing aid demonstration platform can be used portable and outside the clinical setting for algorithmic research. With its behind-the-ear form factor and rechargeable battery, studies can be conducted for several hours without restricting patient movement in real-world scenarios.

Real-Time Multirate Multiband Amplification for Hearing Aids

Hearing loss is a common problem affecting the quality of life for thousands of people. However, many individuals with hearing loss are dissatisfied with the quality of modern hearing aids. Amplification is the main method of compensating for hearing loss in modern hearing aids. One common amplification technique is dynamic range compression, which maps audio signals onto a person's hearing range using an amplification curve. However, due to the frequency dependent nature of the human cochlea, compression is often performed independently in different frequency bands. This paper presents a real-time multirate multiband amplification system for hearing aids, which includes a multirate channelizer for separating an audio signal into eleven standard audiometric frequency bands, and an automatic gain control system for accurate control of the steady state and dynamic behavior of audio compression as specified by ANSI standards. The spectral channelizer offers high frequency resolution with low latency of 5.4 ms and about 14× improvement in complexity over a baseline design. Our automatic gain control includes a closed-form solution for satisfying any designated attack and release times for any desired compression parameters. The increased frequency resolution and precise gain adjustment allow our system to more accurately fulfill audiometric hearing aid prescriptions.

Multirate Audiometric Filter Bank for Hearing Aid Devices

The frequency-dependent nature of hearing loss poses many challenges for hearing aid design. In order to compensate for a hearing aid user's unique hearing loss pattern, an input signal often needs to be separated into frequency bands, or channels, through a process called sub-band decomposition. In this paper, we present a real-time filter bank for hearing aids. Our filter bank features 10 channels uniformly distributed on the logarithmic scale, located at the standard audiometric frequencies used for the characterization and fitting of hearing aids. We obtained filters with very narrow passbands in the lower frequencies by employing multi-rate signal processing. Our filter bank offers a 9.1× reduction in complexity as compared to conventional signal processing. We implemented our filter bank on Open Speech Platform, an open-source hearing aid, and confirmed real-time operation.

Open Speech Platform: Democratizing Hearing Aid Research

Hearing aids help overcome the challenges associated with hearing loss, and thus greatly benefit and improve the lives of those living with hearing-impairment. Unfortunately, there is a lack of adoption of hearing aids among those that can benefit from hearing aids. Hearing researchers and audiologists are trying to address this problem through their research. However, the current proprietary hearing aid market makes it difficult for academic researchers to translate their findings into commercial use. In order to abridge this gap and accelerate research in hearing health care, we present the design and implementation of the Open Speech Platform (OSP), which consists of a co-design of open-source hardware and software. The hardware meets the industry standards and enables researchers to conduct experiments in the field. The software is designed with a systematic and modular approach to standardize algorithm implementation and simplify user interface development. We evaluate the performance of OSP regarding both its hardware and software, as well as demonstrate its usefulness via a self-fitting study involving human participants.

Hearing Aid Self-Adjustment: Effects of Formal Speech-Perception Test and Noise

While listening to recorded sentences with a sound-field level of 65 dB SPL, 24 adults with hearing-aid experience used the "Goldilocks" explore-and-select procedure to adjust level and spectrum of amplified speech to preference. All participants started adjustment from the same generic response. Amplification was provided by a custom-built Master Hearing Aid with online processing of microphone input. Primary goals were to assess the effects of including a formal speech-perception test between repeated self-adjustments and of adding multitalker babble (signal-to-noise ratio +6 dB) during self-adjustment. The speech test did not affect group-mean self-adjusted output, which was close to the National Acoustics Laboratories' prescription for Non-Linear hearing aids. Individuals, however, showed a wide range of deviations from this prescription. Extreme deviations at the first self-adjustment fell by a small but significant amount at the second. The multitalker babble had negligible effect on group-mean self-selected output but did have predictable effects on word recognition in sentences and on participants' opinion regarding the most important subjective criterion guiding self-adjustment. Phoneme recognition in monosyllabic words was better with the generic starting response than without amplification and improved further after self-adjustment. The findings continue to support the efficacy of hearing aid self-fitting, at least for level and spectrum. They do not support the need for inclusion of a formal speech-perception test, but they do support the value of completing more than one self-adjustment. Group-mean data did not indicate a need for threshold-based prescription as a starting point for self-adjustment.

A Wearable Platform for Research in Augmented Hearing

We have previously reported a realtime, open-source speech-processing platform (OSP) for hearing aids (HAs) research. In this contribution, we describe a wearable version of this platform to facilitate audiological studies in the lab and in the field. The system is based on smartphone chipsets to leverage power efficiency in terms of FLOPS/watt and economies of scale. We present the system architecture and discuss salient design elements in support of HA research. The ear-level assemblies support up to 4 microphones on each ear, with 96 kHz, 24 bit codecs. The wearable unit runs OSP Release 2018c on top of 64-bit Debian Linux for binaural HA with an overall latency of 5.6 ms. The wearable unit also hosts an embedded web server (EWS) to monitor and control the HA state in realtime. We describe three example web apps in support of typical audiological studies they enable. Finally, we describe a baseline speech enhancement module included with Release 2018c, and describe extensions to the algorithms as future work.