Frequency Response Masking Based Reconfigurable Filter Bank for Hearing Aids

Shrivastav, Anjali; Kolte, Mahesh T.

doi:10.1007/978-981-19-0863-7_14

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Shrivastav and Kolte [19] proposed a reconfigurable Frequency Response Masking (FRM) filter bank, optimized with a heuristic technique, yielding over a 60% improvement in auditory compensation. Wei et al [20] introduced a nonuniform filter bank (NF) model with 16 bands for hearing aid systems, requiring only three multipliers for executing filter band operations.…”

Section: Literature Surveymentioning

confidence: 99%

“…Increasing filter order typically leads to a rise in power consumption and complexity. Although various digital approaches, such as 2-level audio decompression [17], mono reconfigurable structures [18], and non-uniform filter band models [19,20], have been implemented to improve hearing aid performance, they often encounter issues related to power usage and latency. Furthermore, there is a recognized need to minimize matching errors to provide better auditory compensation, a critical performance metric.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

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

Shrivastav,

Bhandari,

Kolte

2024

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“…Also, the filter bank should provide sharp transitions with minimum filter order and reduced complexity. So, FRM is an apt technique to meet these HA requirements [4]. HA is sought by people who are hard of hearing to hear sounds clearly in various environments, including quiet and noisy settings.…”

Section: Introductionmentioning

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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