Speech Noise Reduction Algorithm in Digital Hearing Aids Based on an Improved Sub-band SNR Estimation

To improve the speech intelligibility in noisy environments for persons with hearing impairments, a new method for reducing noise, based on improved sub-band signal-to-noise ratio (SNR) estimation, is proposed. First, the input signal is decomposed into several sub-band signals with an analysis filter bank. Then, under the assumption of a Gaussian model, maximum a posterior probability is applied to estimate the information embedded in adjacent frames in each sub-band, which is in the form of a joint probability density function, and the minimum of the noise spectrum is tracked to estimate the noise. Subsequently, the gain of each sub-band, which changes with the noise in the corresponding sub-band, is calculated with a linear proportional gain function. The obtained gains of the sub-bands are multiplied by the sub-band noisy signals to obtain the enhanced sub-band speech signals. Finally, all the sub-band signals are spliced to obtain the estimated speech signals. In this algorithm, the gains are calculated in the time domain, which avoids the process of the inverse Fourier transform and leads to a decrease in computational complexity. Compared with the traditional spectral subtraction and basic Wiener filtering method, the delay in this algorithm is reduced by 40.4 and 60.6%, respectively. It is also compared with the modulation depth integrated into hearing aids under an experimental simulation and a real scenario. The results indicate that the output SNR is improved by 1 dB under the software simulation and 3.1 dB in the real scenario when the input SNR is set as 10 dB. Compared with the simulation environment, the proposed algorithm only fell by 1.5% in the real scenario. Furthermore, the distance of the logarithmic spectrum and quality of speech perception are improved by 20.6 and 9.3%, respectively.