mlsanityData to 14 Oct 2026

Wavetable Synthesis Using CVAE for Timbre Control Based on Semantic Label

arXiv 2410.18628 ↗First posted 24 Oct 2024cs.SD cs.AI eess.AScited by 0

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Abstract (arXiv)

Synthesizers are essential in modern music production. However, their complex timbre parameters, often filled with technical terms, require expertise. This research introduces a method of timbre control in wavetable synthesis that is intuitive and sensible and utilizes semantic labels. Using a conditional variational autoencoder (CVAE), users can select a wavetable and define the timbre with labels such as bright, warm, and rich. The CVAE model, featuring convolutional and upsampling layers, effectively captures the wavetable nuances, ensuring real-time performance owing to their processing in the time domain. Experiments demonstrate that this approach allows for real-time, effective control of the timbre of the wavetable using semantic inputs and aims for intuitive timbre control through data-based semantic control.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 35 references that are in the map. First posted on arXiv 24 Oct 2024.

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References · 8

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2014Semi-Supervised Learning with Deep Generative Modelscited by 496
  4. 2017Neural Audio Synthesis of Musical Notes with WaveNet Autoencoderscited by 225
  5. 2019Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishingcited by 191
  6. 2017Fader Networks: Manipulating Images by Sliding Attributescited by 118
  7. 2019GANSynth: Adversarial Neural Audio Synthesiscited by 116
  8. 2021RAVE: A variational autoencoder for fast and high-quality neural audio synthesiscited by 73

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