SmartHeaP - A High-level Programmable, Low Power, and Mixed-Signal Hearing Aid SoC in 22nm FD-SOI

Karrenbauer, Jens; Klein, Simon; Schonewald, Sven; Gerlach, Lukas; Blawat, Meinolf; Benndorf, Jens; Blume, Holger

doi:10.1109/esscirc55480.2022.9911325

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…Generally, a model configuration that fits hardware constraints of a desired platform could be chosen without sacrificing too much performance. With the decrease in model parameters and computational complexity reported here, the smallest separation model should already be compatible with next-gen hearing-aid system-on-chip (SOC) such as [34] if weights and operations are quantized to 8-bit integers.…”

Section: Effect Of Model Size and Groupingmentioning

confidence: 99%

Binaural Multichannel Blind Speaker Separation With a Causal Low-Latency and Low-Complexity Approach

Westhausen,

Meyer

2024

IEEE Open J. Signal Process.

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In this article, we introduce a causal low-latency low-complexity approach for binaural multichannel blind speaker separation in noisy reverberant conditions. The model, referred to as Group Communication Binaural Filter and Sum Network (GCBFSnet) predicts complex filters for filter-and-sum beamforming in the time-frequency domain. We apply Group Communication (GC), i.e., latent model variables are split into groups and processed with a shared sequence model with the aim of reducing the complexity of a simple model only containing one convolutional and one recurrent module. With GC we are able to reduce the size of the model by up to 83% and the complexity up to 73% compared to the model without GC, while mostly retaining performance. Even for the smallest model configuration, GCBFSnet matches the performance of a low-complexity TasNet baseline in most metrics despite the larger size and higher number of required operations of the baseline.INDEX TERMS Binaural, low-latency, multi-channel, real-time, speaker-separation.

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Section: Effect Of Model Size and Groupingmentioning

confidence: 99%

Binaural Multichannel Blind Speaker Separation With a Causal Low-Latency and Low-Complexity Approach

Westhausen,

Meyer

2024

IEEE Open J. Signal Process.

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“…One core for wireless interfacing and a computational, more advanced core for audio processing. As shown in [10], the audio core of the SoC is capable of executing complex hearing aid algorithms without reaching the computational limits of the architecture. The essential features of the SmartHeaP SoC are listed in the table I.…”

Section: A Audio Processing and System Control Unitmentioning

confidence: 99%

“…The platform consists of all the necessary components to evaluate different real-world scenarios. It is based on a newly developed, programmable, 50 MHz Smart Hearing Aid Processor (SmartHeaP) system-on-chip (SoC) [10], two micro-electromechanical systems (MEMS) microphones, a speaker, and two different wireless communication technologies. Everything is embedded in a hearing aid case that is less than two 50-euro cent coins in size, so it fits easily behind the ear.…”

Section: Introduction and Related Workmentioning

confidence: 99%

A High-Performance, Low Power Research Hearing Aid featuring a High-Level Programmable Custom 22nm FDSOI SoC^*

Karrenbauer,

Schönewald,

Klein

et al. 2023

2023 45th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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With advances in algorithmic hearing aid research, the need for high-level programmable, behindthe-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 realworld scenarios.

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“…One important functional block that is a part of hearing aids [1][2][3][4][5][6] as well as devices for human voice recognition [7], are tunable filters [8][9][10][11] that emulate characteristics of a human cochlea. Such filters allow pre-processing of acoustic signals based on extracting components of the amplitude spectrum of the received sounds and enabling adaptive adjustment of the sensitivity of the signal path in individual frequency bands.…”

Section: Introductionmentioning

confidence: 99%

A 0.5 V Nanowatt Biquadratic Low-Pass Filter with Tunable Quality Factor for Electronic Cochlea Applications

Jakusz,

Jendernalik

2024

Electronics

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A novel implementation of an analogue low-power, second-order, low-pass filter with tunable quality factor (Q) is presented and discussed. The filter feature is a relatively simple, buffer-based, circuit network consisting of eleven transistors operating in a subthreshold region. Q tuning is accomplished by injecting direct current into a network node, which changes the output resistance of the transistors and, as a result, modifies the filter network’s loss, and thus its Q. Q tuning is independent of a filter cut-off frequency (ω0). The filter, with a nominal ω0 of 1 kHz, was fabricated using a 0.18 µm CMOS technology, and features a Q range of 2–11, power consumption of up to 52 nW, and a 59 dB dynamic range when using a 0.5 V supply. The ω0 can be tuned from 0.5 to 2.5 kHz using a traditional method by changing the transistor transconductances, but this process partially affects the quality factor.

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SmartHeaP - A High-level Programmable, Low Power, and Mixed-Signal Hearing Aid SoC in 22nm FD-SOI

Cited by 7 publications

References 17 publications

Binaural Multichannel Blind Speaker Separation With a Causal Low-Latency and Low-Complexity Approach

Binaural Multichannel Blind Speaker Separation With a Causal Low-Latency and Low-Complexity Approach

A High-Performance, Low Power Research Hearing Aid featuring a High-Level Programmable Custom 22nm FDSOI SoC^*

A 0.5 V Nanowatt Biquadratic Low-Pass Filter with Tunable Quality Factor for Electronic Cochlea Applications

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SmartHeaP - A High-level Programmable, Low Power, and Mixed-Signal Hearing Aid SoC in 22nm FD-SOI

Cited by 7 publications

References 17 publications

Binaural Multichannel Blind Speaker Separation With a Causal Low-Latency and Low-Complexity Approach

Binaural Multichannel Blind Speaker Separation With a Causal Low-Latency and Low-Complexity Approach

A High-Performance, Low Power Research Hearing Aid featuring a High-Level Programmable Custom 22nm FDSOI SoC*

A 0.5 V Nanowatt Biquadratic Low-Pass Filter with Tunable Quality Factor for Electronic Cochlea Applications

Contact Info

Product

Resources

About

A High-Performance, Low Power Research Hearing Aid featuring a High-Level Programmable Custom 22nm FDSOI SoC^*