层次化的木材绘制和清晰度生成
Hierarchical Timbre-Painting and Articulation Generation
摘要：
我们提出了一种快速和高保真的音乐生成方法，根据指定的f0和响度，使合成的音频模仿目标乐器的音色和清晰度。生成过程由学习的源过滤网络组成，该网络以更高的分辨率重建信号。该模型将多分辨率频谱损失优化为重建损失，将对抗性损失优化为使音频声音更逼真，将感知f0损失优化为使输出与所需输入音高轮廓对齐。所提出的架构能够实现高质量的仪器拟合，给定的样本可以短至几分钟，并且该方法展示了最先进的音色传输能力。代码和音频示例在https://github.com/mosheman5/timbre_painting上共享。
英文摘要：
We present a fast and high-fidelity method for music generation, based on specified f0 and loudness, such that the synthesized audio mimics the timbre and articulation of a target instrument. The generation process consists of learned source-filtering networks, which reconstruct the signal at increasing resolutions. The model optimizes a multi-resolution spectral loss as the reconstruction loss, an adversarial loss to make the audio sound more realistic, and a perceptual f0 loss to align the output to the desired input pitch contour. The proposed architecture enables high-quality fitting of an instrument, given a sample that can be as short as a few minutes, and the method demonstrates state-of-the-art timbre transfer capabilities. Code and audio samples are shared at https://github.com/mosheman5/timbre_painting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
