Enhanced Hearing Aid Performance with an African Buffalo Optimization-Based Frequency Response Masking Filter
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".