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Title: | Accurate estimation of decay coefficients for dynamic range compressors in hearing aids and a hardware level comparison of different architectures |
Authors: | Deepu, S.P. Kini, M.R. Sumam, David S. |
Issue Date: | 2020 |
Citation: | Microprocessors and Microsystems, 2020, Vol.74, , pp.- |
Abstract: | Dynamic Range Compression (DRC) algorithm helps to protect the residual hearing ability of hearing aid users by compressing the signal levels which go above a particular threshold. This paper addresses two different aspects of DRC for hearing aid applications. In the first part, methods to estimate the decay coefficients corresponding to the required time constants for a feed-forward DRC architecture accurately, to meet the hearing aid specifications are proposed. The effect of compression on the attack and release time parameters are compensated with the new formula. The hardware implementation of four different DRC architectures is explained in the second part of the paper. The estimated decay coefficients for a test signal were used for the corresponding hardware implementations and verified the validity of proposed algorithmic modifications. The architectures were implemented using UMC 65 nm standard cell libraries and the power and error results were compared. The proposed methods to estimate the decay coefficients for both attack and release phases show close to 0 dB error from expected output values, while conventional methods are not meeting the specifications. Hardware implementation shows that there is not much improvement in power performance, between a lower resolution Look-Up Table (LUT) based logarithm implementation and a higher resolution one. From the results, we propose using the absolute level detector based DRC with higher resolution logarithm without a gain smoothing stage at the output for lowest power consumption and better approximation error performance. 2020 Elsevier B.V. |
URI: | 10.1016/j.micpro.2019.102967 https://idr.nitk.ac.in/jspui/handle/123456789/9773 |
Appears in Collections: | 1. Journal Articles |
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