![]() Where is the smoothed noise estimate in i-th frame, λ n is the filtering coefficient (0.5 ≤ λ n ≤ 0.9, some authors use values 0.8 ≤ λ n ≤ 0.95). Noise estimate in k-th frame may be obtained by filtering the noise using first-order low-pass filter: Where is the amplitude spectrum of the i-th of the K frames of noise. The noise spectrum estimate is related to the expected noise spectrum which is usually calculated using the time-averaged noise spectrum taken from parts of the recording where only noise is present. ![]() The statistic parameters of the noise are not known, thus the noise and the speech signal are replaced by their estimates: Where Y(jω), X(jω), N(jω) are Fourier transforms of y(m), x(m), n(m), respectively. In the frequency domain, this may be denoted as: The noisy signal y(m) is a sum of the desired signal x(m) and the noise n(m): It is assumed that the signal is distorted by a wide-band, stationary, additive noise, the noise estimate is the same during the analysis and the restoration and the phase is the same in the original and restored signal. In this method, an average signal spectrum and average noise spectrum are estimated in parts of the recording and subtracted from each other, so that average signal-to-noise ratio (SNR) is improved. The spectral subtraction method is a simple and effective method of noise reduction. This module is based on the spectral subtraction performed independently in the frequency bands corresponding to the auditory critical bands. ![]() The noise reduction module intends to lower the noise level without affecting the speech signal quality. The background noise is the most common factor degrading the quality and intelligibility of speech in recordings. Multitask Noisy Speech Enhancement System ![]()
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