An Efficient Reconfigurable FRM Filterbank for Digital Hearing Aid

Shrivastav, Anjali; Kolte, Mahesh T.; Bhandari, Sheetal U.; Harpale, Varsha K.

doi:10.1109/iccubea54992.2022.10010721

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…This model demonstrated a lower delay ratio, though it was complex in design. Shrivastav et al [21] designed a reconfigurable FRM filter utilizing three decomposition levels and optimized it with a swarm-based algorithm. Fan Zhang et al [23] designed a unified modulated filter bank aiming to reduce complexity by minimizing the use of multipliers, but this approach resulted in lower hardware accuracy.…”

Section: Literature Surveymentioning

confidence: 99%

Enhanced Hearing Aid Performance with an African Buffalo Optimization-Based Frequency Response Masking Filter

Shrivastav,

Bhandari,

Kolte

2024

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Hearing aid systems rely on reconfigurable filters to selectively amplify desired signals while suppressing unwanted noise. However, the complexity of the hardware and the associated power dissipation present substantial challenges, particularly in systems where adaptability and scalability are crucial. These systems require advanced auditory compensation to accommodate the diverse auditory profiles of individuals with hearing impairments. In this study, an African buffalo-inspired optimization algorithm is introduced to fine-tune a Frequency Response Masking Reconfigurable Filter (AB-FRMRF). This novel approach ensures High-Q filtering with narrow transition bands while maintaining a low filter order, thereby enhancing selectivity and minimizing complexity. The optimization algorithm adjusts its search strategy based on solution fitness, optimizing the filter's coefficient values. Performance parameters such as matching error and group delay are meticulously tuned for optimal auditory compensation. Utilizing distributed arithmetic for hardware realization and a Brent Kung adder for the summation of dynamic random-access memory (DRAM) partial products, the hardware design deviates from conventional pipeline adder trees, culminating in significant power savings and improved processing speed. Modeled in MATLAB and implemented on a Kintex 7 FPGA Genesys 2 board, the AB-FRMRF model exhibits a reduced matching error down to 1.2 dB and a minimized delay to 2.5 msec for high-frequency sounds in cases of mild hearing loss. These metrics are favorable in comparison to existing reconfigurable filters reported in the literature. The AB-FRMRF model not only demonstrates a 35% improvement in power consumption but also outperforms traditional methods in computational complexity, auditory compensation, delay, and power metrics, making it highly suitable for hearing aid applications. The proposed filter design signifies a substantial advancement in auditory assistance technology, aligning with the necessity for high-performance, low-power hearing aid devices.

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Section: Literature Surveymentioning

confidence: 99%

Enhanced Hearing Aid Performance with an African Buffalo Optimization-Based Frequency Response Masking Filter

Shrivastav,

Bhandari,

Kolte

2024

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“…Based on the exhaustive literature review of the existing reconfigurable filter banks, it can be concluded that a reconfigurable filter bank with 16 bands is the best in terms of power efficiency and output delay. Further, we use the Frequency Response Masking technique [18] to enhance the output sound experience and reduce the matching error. These will be more effective in hearing aids owing to the adaptive hearing characteristics of people.…”

Section: Related Workmentioning

confidence: 99%

Reconfigurable Multiband Dynamic Range Compression-based FRM Filter for Hearing Aid

Anjali Shrivastav

2023

IJRITCC

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In this research, we present an innovative method for enhancing the performance of hearing aids using a Multiband Dynamic Range Compression-based Reconfigurable Frequency Response Masking (FRM) Filterbank. First, a unform16-band reconfigurable filter bank, which is reconfigurable, is designed utilizing the FRM scheme. The strategic arrangement of each sub-band within the proposed filter bank is meticulously prepared to optimize the matching performance. Based on the hearing characteristics of patients, the sub-bands can be distributed in low, medium, and high-frequency regions. Also, the gain can be adjusted per the patient's hearing profile from their audiogram for better auditory compensation. Further, the Multiband Dynamic Range Compression (MBDRC) technique is applied to address the specific needs of individuals with different frequency-dependent hearing impairments. It involves using dynamic range compression independently to different frequency sub-bands within a filter bank. In MBDRC, the compression parameters, such as compression threshold and ratio, can be adjusted independently for every subband. It allows for a more tailored approach to address the specific hearing needs of different frequency regions. If an individual has more severe hearing loss in high-frequency regions, higher compression ratios and lower compression thresholds can be applied to those subbands to amplify and improve audibility for high-frequency sounds. Once dynamic range compression is applied to each sub-band, the resultant sub-bands are reassembled to yield the ultimate output signal, which can subsequently be transmitted to the speaker or receiver of the hearing aid. A GUI can be helpful for better visualization and parameter control, including gain adjustment and compression parameters of this entire process. With this aim in mind, a GUI has been developed on MATLAB. Different audio files can be imported, and their frequency response can be generated and observed. Based on a person's audiogram, the control parameters can be set to low, medium, or high. Their sub-band distribution in low, medium, and high-frequency regions can be visualized. Further, the filter bank makes automatic gain adjustments, as seen in the GUI. The gain points for each band can also be manually adjusted according to users' hearing characteristics to minimize the error. Also, the compression parameters can be set separately for each subband as per the hearing requirement of the patient. Further, the processed output can be visualized in the output frequency response tab, and the input and output audio signals can be analyzed.

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“…Consequently, loss of hearing can severely affect, not only our ability to function effectively, but also lead to social alienation. Loss of hearing affects about a fifth of the human population [1,2,3], and can be attributed to to a plethora of reasons including congenital factors, disease, accidents or simply aging [2]. Hearing aids (HAs) are the most widely used devices to improve sound perception in those with hearing difficulty.…”

Section: Introductionmentioning

confidence: 99%

Towards an FPGA Implementation of IOT-Based Multi-Modal Hearing AID System

Enemali

Bishnu

Ratnarajah

et al. 2023

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)

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This paper presents work in progress with aims to develop a first of its kind IoT Cloud-based multimodal hearing aid (HA) prototype. Specifically, it identifies the major processing blocks for implementation on an embedded Field Programmable Gate Array (FPGA) and focuses on aspects of a custom block that deals with the integration of multiple input sources of the HA. In particular, the integration block deals with the combination of audio and video (AV) data at different sampling rates, with each having a different number of bits to encode their information. This realises a seamless combination of AV data by assigning a scaled number of bits to each component in the combined signal to achieve real-time data transmission of all data sources. Finally, we outline ongoing work aiming to address a range of key challenges including required optimisation of tradeoffs between HA latency, streaming bandwidth, security, privacy, power consumption and performance.

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An Efficient Reconfigurable FRM Filterbank for Digital Hearing Aid

Cited by 5 publications

References 14 publications

Enhanced Hearing Aid Performance with an African Buffalo Optimization-Based Frequency Response Masking Filter

Enhanced Hearing Aid Performance with an African Buffalo Optimization-Based Frequency Response Masking Filter

Reconfigurable Multiband Dynamic Range Compression-based FRM Filter for Hearing Aid

Towards an FPGA Implementation of IOT-Based Multi-Modal Hearing AID System

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