02178nas a2200229   4500000000100000000000100001008004100002260001200043653002300055653001800078653002000096653002800116653002200144100001700166700002700183245007800210856010000288300001000388490000600398520153000404022001401934       2020                            d  c03/202010aSpeech Enhancement10aDeep Learning10aIntelligibility10aTime- Frequency Masking10aIdeal Binary Mask1 aNasir Saleem1 aMuhammad Irfan Khattak00aDeep Neural Networks for Speech Enhancement in Complex-Noisy Environments  uhttps://www.ijimai.org/journal/sites/default/files/files/2019/06/ijimai20206_1_10_pdf_20073.pdf  a84-900 v63 aIn this paper, we considered the problem of the speech enhancement similar to the real-world environments where several complex noise sources simultaneously degrade the quality and intelligibility of a target speech. The existing literature on the speech enhancement principally focuses on the presence of one noise source in mixture signals. However, in real-world situations, we generally face and attempt to improve the quality and intelligibility of speech where various complex stationary and nonstationary noise sources are simultaneously mixed with the target speech. Here, we have used deep learning for speech enhancement in complex-noisy environments and used ideal binary mask (IBM) as a binary classification function by using deep neural networks (DNNs). IBM is used as a target function during training and the trained DNNs are used to estimate IBM during enhancement stage. The estimated target function is then applied to the complex-noisy mixtures to obtain the target speech. The mean square error (MSE) is used as an objective cost function at various epochs. The experimental results at different input signal-to-noise ratio (SNR) showed that DNN-based complex-noisy speech enhancement outperformed the competing methods in terms of speech quality by using perceptual evaluation of speech quality (PESQ), segmental signal-to-noise ratio (SNRSeg), log-likelihood ratio (LLR), weighted spectral slope (WSS). Moreover, short-time objective intelligibility (STOI) reinforced the better speech intelligibility.  a1989-1660