In deep learning research, many melody extraction models rely on redesigning neural network architectures to improve performance. In this paper, we propose an input feature modification and a training objective modification based on two assumptions. First, harmonics in the spectrograms of audio data decay rapidly along the frequency axis. To enhance the model's sensitivity on the trailing harmonics, we modify the Combined Frequency and Periodicity (CFP) representation using discrete z-transform. Second, the vocal and non-vocal segments with extremely short duration are uncommon. To ensure a more stable melody contour, we design a differentiable loss function that prevents the model from predicting such segments. We apply these modifications to several models, including MSNet, FTANet, and a newly introduced model, PianoNet, modified from a piano transcription network. Our experimental results demonstrate that the proposed modifications are empirically effective for singing melody extraction.

翻译：在深度学习研究中，许多旋律提取模型依赖于重新设计神经网络架构以提升性能。本文基于两个假设提出输入特征修改与训练目标修改。首先，音频数据语谱图中的谐波沿频率轴快速衰减。为增强模型对尾部谐波的敏感性，我们利用离散z变换对组合频率与周期性（CFP）表示进行修改。其次，时长极短的 vocal 与非 vocal 片段并不常见。为确保更稳定的旋律轮廓，我们设计了一种可微损失函数，阻止模型预测此类片段。我们将这些修改应用于多个模型，包括 MSNet、FTANet 以及新引入的源自钢琴转录网络的 PianoNet 模型。实验结果表明，所提出的修改对歌声旋律提取具有实证有效性。