论文标题
Vapar合成器 - 音频综合的差异参数模型
VaPar Synth -- A Variational Parametric Model for Audio Synthesis
论文作者
论文摘要
随着数据驱动的统计建模和丰富的计算能力的出现，研究人员越来越多地转向音频综合的深度学习。这些方法试图直接在时间或频域中建模音频信号。为了对生成的声音进行更灵活的控制，使用信号的参数表示可能更有用，该信号更直接地与音乐属性（例如音调，动力学和音色）相对应。我们提出Vapar合成器 - 一种变异参数合成器，该合成器利用对适当的参数表示训练的条件变异自动编码器（CVAE）。我们通过重建和产生仪器的音调来证明我们提出的模型的功能，并能够灵活控制其音高。
With the advent of data-driven statistical modeling and abundant computing power, researchers are turning increasingly to deep learning for audio synthesis. These methods try to model audio signals directly in the time or frequency domain. In the interest of more flexible control over the generated sound, it could be more useful to work with a parametric representation of the signal which corresponds more directly to the musical attributes such as pitch, dynamics and timbre. We present VaPar Synth - a Variational Parametric Synthesizer which utilizes a conditional variational autoencoder (CVAE) trained on a suitable parametric representation. We demonstrate our proposed model's capabilities via the reconstruction and generation of instrumental tones with flexible control over their pitch.
