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• An efficient hybrid network for supercontinuum generation prediction proposed. • Wasserstein generative adversarial network augments the original dataset. • Deep neural network with a TCN layer predicts supercontinum spectrum accurately. In this paper, a Hybrid Network is reported for predicting the output pulse shape parameters of the supercontinuum in optical fibers. The network incorporates the Wasserstein generative adversarial network (WGAN) and the deep neural network (DNN) with a TCN layer. The WGAN augments a limited set of original data, while the designed DNN accurately predicts supercontinuum. The simulation results show the spectra agreement (A gm) of the predictions by individual DNN is 89% and the root mean square error (RMSE) is 0.1092. After retraining with WGAN's extended dataset, the A gm of DNN predictions is improved to 98% and the RMSE is reduced to 0.0513. The input pump power corresponding to the appearance of the fourth soliton peak can be predicted by the retrained DNN. Numerical analysis demonstrates the effectiveness and generalization ability of a Hybrid Network in predicting pulse parameters. The network fits the propagation dynamics in the spectral domain of SC, which provides a promising tool for further research in this field. [ABSTRACT FROM AUTHOR]

• A novel approach for single channel speech enhancement is presented, using the colored spectrograms. • Using deep neural network for spectrogram denoising and regression neural network for translating the colors of denoised spectrograms to short time Fouier transform (STFT) magnitudes. • Output is comparable to the state-of-the-art baseline models at a much reduced computational cost. Speech enhancement concerns the processes required to remove unwanted background sounds from the target speech to improve its quality and intelligibility. In this paper, a novel approach for single-channel speech enhancement is presented using colored spectrograms. We propose the use of a deep neural network (DNN) architecture adapted from the pix2pix generative adversarial network (GAN) and train it over colored spectrograms of speech to denoise them. After denoising, the colors of spectrograms are translated to magnitudes of short-time Fourier transform (STFT) using a shallow regression neural network. These estimated STFT magnitudes are later combined with the noisy phases to obtain an enhanced speech. The results show an improvement of almost 0.84 points in the perceptual evaluation of speech quality (PESQ) and 1 % in the short-term objective intelligibility (STOI) over the unprocessed noisy data. The gain in quality and intelligibility over the unprocessed signal is almost equal to the gain achieved by the baseline methods used for comparison with the proposed model, but at a much reduced computational cost. The proposed solution offers a comparative PESQ score at almost 10 times reduced computational cost than a similar baseline model that has generated the highest PESQ score trained on grayscaled spectrograms, while it provides only a 1 % deficit in STOI at 28 times reduced computational cost when compared to another baseline system based on convolutional neural network-GAN (CNN-GAN) that produces the most intelligible speech. [ABSTRACT FROM AUTHOR]