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A Hybrid Neural Network Based on the Duplex Model of Pitch Perception for Singing Melody Extraction

机译:一种基于对歌唱旋律提取的双工模型的混合神经网络

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In this paper, we build up a hybrid neural network (NN) for singing melody extraction from polyphonic music by imitating human pitch perception. For human hearing, there are two pitch perception models, the spectral model and the temporal model, in accordance with whether harmonics are resolved or not. Here, we first use NNs to implement individual models and evaluate their performance in the task of singing melody extraction. Then, we combine the NNs to constitute the composite NN to simulate the duplex model, which complements the pitch perception from unresolved harmonics of the spectral model using the temporal model. Simulation results show the proposed composite NN outperforms other conventional methods in singing melody extraction.
机译:在本文中,我们通过模仿人的俯仰感知来构建一个混合神经网络(NN),用于通过模拟人的俯仰感知来唱歌从多相音乐中的旋律提取。对于人类听力,根据谐波是否已解决,有两个俯仰感知模型,光谱模型和时间模型。在这里,我们首先使用NNS实现各个模型,并在唱歌旋律提取的任务中评估它们的性能。然后,我们将NN组合成构成复合Nn以模拟双工模型,其补充了使用时间模型的光谱模型的未解决谐波的音调感知。仿真结果表明,所提出的复合Nn优于唱歌旋律提取的其他常规方法。

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