Enhancement of Speech Signals for Hearing Aid Devices using Digital Signal Processing

Zaman, Khalid; Maghdid, Sozan Sulaiman; Afridi, Hammad; Ullah, Sabih; Zohaib, Muhammad

doi:10.1109/ismsit50672.2020.9255299

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…The echo cancellation in signal processing is majorly played by the adaptive filter. So Zaman et al [26] introduced a narrow transition band (NTB) design using distributed arithmetic. This approach is realized using the shift accumulates operations and look-up tables, and also it replaces the decimation filter with the single DA unit.…”

Section: Related Workmentioning

confidence: 99%

Aquila Based Adaptive Filtering for Hearing Aid with Optimized Performance

2023

IJIES

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In recent days, one of the most serious health problems in humans is hearing loss. For helping the hearing impairment, the Hearing Aid (HA) system is used, but it may lack in various aspects such as delay, matching error, power consumption, low complexity and unwanted noise. Many techniques were introduced to overcome these issues, but they still have problems. Therefore, this research uses a novel Aquila-based Adaptive Filter (AbAF) for improving the HA system with the optimized parameters of filter. Here, the incorporation of the Aquila optimizer along with the adaptive filter is considered as the key novelty of this present work. The main motive of this proposed model is to filter the noisy features more possible range for improve the HA performance. Hence, the Aquila agent removes the unwanted noise by updating the adaptive processor's filter coefficient and optimizing the filter parameters such as matching error, delay, complexity, and bandwidth and power consumption. Moreover, the suggested model is designed and executed in the MATLAB platform. Finally, the performance parameters were estimated and compared with the past studies to validate the performance improvement. The presented model scored the reduced Matching error as 0.3% and reduced power consumption as 10%, which quite lower than the compared models.

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Section: Related Workmentioning

confidence: 99%