Five new derivatives of 4,6-di(thiophen-2-yl)pyrimidine (DTP) were designed by structural modification with the aim to tune the electro-optical and charge transfer properties. The effect of oligocene and oligocenothiophene incorporation/substitution was investigated on various properties of interests. The smaller hole reorganization energy revealed that compounds 1-5 might be good hole transfer contenders. The smaller hole reorganization energy of newly designed five DTP derivatives than the pentacene showed that prior compounds might be good/comparable hole transfer materials than/to that of pentacene. The computed electron reorganization energy of DTP derivatives 1-5 are 124, 185, 93, 95 and 189 meV smaller than the meridional-tris (8-hydroxyquinoline) aluminum (mer-Alq3) illuminating that electron mobility of these derivatives might be better/comparable than/to referenced compound.
Speech enhancement is fundamental for various real time speech applications and it is a challenging task in the case of a single channel because practically only one data channel is available. We have proposed a supervised single channel speech enhancement algorithm in this paper based on a deep neural network (DNN) and less aggressive Wiener filtering as additional DNN layer. During the training stage the network learns and predicts the magnitude spectrums of the clean and noise signals from input noisy speech acoustic features. Relative spectral transform-perceptual linear prediction (RASTA-PLP) is used in the proposed method to extract the acoustic features at the frame level. Autoregressive moving average (ARMA) filter is applied to smooth the temporal curves of extracted features. The trained network predicts the coefficients to construct a ratio mask based on mean square error (MSE) objective cost function. The less aggressive Wiener filter is placed as an additional layer on the top of a DNN to produce an enhanced magnitude spectrum. Finally, the noisy speech phase is used to reconstruct the enhanced speech. The experimental results demonstrate that the proposed DNN framework with less aggressive Wiener filtering outperforms the competing speech enhancement methods in terms of the speech quality and intelligibility.