Abstract
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.
Highlights
In a natural environment rich in sound emanating from multiple sources, a target sound reaching our ears is usually mixed with other acoustic interference
The results indicate that the proposed method robust principal component analysis (RPCA)-deep recurrent neural networks (DRNNs) is superior to all of the reference methods in global normalized source-todistortion ratio (SDR) (NSDR) (GNSDR) and global source-toartifact ratio (SAR) (GSAR)
A method combining RPCA and supervised DRNN was employed in an experiment to improve the separation of singing voice from musical accompaniment in monophonic mixtures
Summary
In a natural environment rich in sound emanating from multiple sources, a target sound reaching our ears is usually mixed with other acoustic interference. We propose using RPCA based on the underlying low-rank and sparse properties of accompaniments and vocals, respectively, to achieve the initial separation and apply supervised DRNN to limited data to further separate the results of RPCA in order to further correct misclassified or residual singing and background music from the initial separation. The resulting sparse and low-rank matrices obtained after RPCA and postprocessing are sent to their corresponding sRNNs. One sRNN further separates the sparse matrix into the estimated singing and musical accompaniment parts because there is a residual background music component in the initial separated sparse matrix.
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