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.

中文翻译：

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用于单耳歌声分离的 RPCA-DRNN 技术

在这项研究中，我们提出了一种在单声道音乐混合中将歌声与音乐伴奏分离的方法。所提出的方法使用鲁棒主成分分析 (RPCA)，然后进行后处理，包括中值滤波器、形态学和高通滤波器，以分解混合物。随后，深度递归神经网络由两个联合优化的并行堆叠递归神经网络 (sRNNs) 和掩膜层组成，并在有限的数据和计算上进行训练，应用于分解的组件，以优化最终估计的分离的歌声和背景音乐，以进一步纠正在最初的分离中错误分类或残留歌声和背景音乐。MIR-1K、ccMixter、和 MUDB18 数据集以及与 10 种现有技术的比较表明，所提出的方法在单耳歌声分离方面取得了有竞争力的表现。在 MUDB18 上，与其他最先进的技术相比，所提出的方法以更少的训练数据和更低的计算成本达到了相当的分离质量。