In this study, we propose a methodology for separating a singing voice from musical accompaniment in a monaural musical mixture. The proposed method uses robust principal component analysis (RPCA), followed by postprocessing, including median filter, morphology, and high-pass filter, to decompose the mixture. Subsequently, a deep recurrent neural network comprising two jointly optimized parallel-stacked recurrent neural networks (sRNNs) with mask layers and trained on limited data and computation is applied to the decomposed components to optimize the final estimated separated singing voice and background music to further correct misclassified or residual singing and background music in the initial separation. The experimental results of MIR-1K, ccMixter, and MUSDB18 datasets and the comparison with ten existing techniques indicate that the proposed method achieves competitive performance in monaural singing voice separation. On MUSDB18, the proposed method reaches the comparable separation quality in less training data and lower computational cost compared to the other state-of-the-art technique.

Speech segregation is one of the most difficult tasks in speech processing. This paper uses computational auditory scene analysis, support vector machine classifier, and post-processing on binary mask to separate speech from background noise. Melfrequency cepstral coefficients and pitch are the two features used for support vector machine classification. Connected Component Labeling, Hole Filling, and Morphology are applied on the resulting binary mask as post-processing. Experimental results show that our method separates speech from background noise effectively.

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