Adaptive recurrent nonnegative matrix factorization with phase compensation for Single-Channel speech enhancement

The speech signals are affected by the background noise distortion that is unfavorable to both the intelligibility as well as the speech quality. Most of the speech processing algorithms function with the spectral magnitude without consideration of the spectral phase by leaving them unexplored and unstructured. The proposed single channel speech enhancement model called the Adaptive Recurrent Nonnegative Matrix Factorization (AR-NMF) is designed based on the phase compensation strategy with deep learning. The two major phases considered here are the training phase and the testing phase. During the process of training, the noisy speech signal is decomposed by the Hurst exponent-based Empirical Mode Decomposition (HEMD) and is converted into the frequency domain using Short Time Fourier Transform. Further, the new AR-NMF is used for denoising, where the tuning factor is optimally generated by the optimized RNN. Here, the hidden neurons are optimized using the proposed Adaptive Attack Power-based Sail Fish Optimization (AAP-SFO) with consideration of minimizing the Mean Absolute Error between the actual value and the predicted value. Finally, this phase compensated speech signal is given to the ISTFT that results in the final denoised clean speech signal. From the analysis, the CSED of AAP-SFO-AR-NMF for the street noise is 58.24%, 57.34%, 56.72%, and 77.37% more than RNMF, esHRNR, esTSNR, and Vuvuzela respectively. The performance of the proposed deep enhancement method is extensively evaluated and compared to diverse adverse noisy environments that describe the superiority of the proposed method.